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Kwok WH, Zhang Y, Wang G. Artificial intelligence in perinatal mental health research: A scoping review. Comput Biol Med 2024; 177:108685. [PMID: 38838557 DOI: 10.1016/j.compbiomed.2024.108685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 04/28/2024] [Accepted: 06/01/2024] [Indexed: 06/07/2024]
Abstract
The intersection of Artificial Intelligence (AI) and perinatal mental health research presents promising avenues, yet uncovers significant challenges for innovation. This review explicitly focuses on this multidisciplinary field and undertakes a comprehensive exploration of existing research therein. Through a scoping review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we searched relevant literature spanning a decade (2013-2023) and selected fourteen studies for our analysis. We first provide an overview of the main AI techniques and their development, including traditional methods across different categories, as well as recent emerging methods in the field. Then, through our analysis of the literature, we summarize the predominant AI and ML techniques adopted and their applications in perinatal mental health studies, such as identifying risk factors, predicting perinatal mental health disorders, voice assistants, and Q&A chatbots. We also discuss existing limitations and potential challenges that hinder AI technologies from improving perinatal mental health outcomes, and suggest several promising directions for future research to meet real needs in the field and facilitate the translation of research into clinical settings.
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Affiliation(s)
- Wai Hang Kwok
- School of Nursing and Midwifery, Edith Cowan University, WA, Australia
| | - Yuanpeng Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, China
| | - Guanjin Wang
- School of Information Technology, Murdoch University, Murdoch, WA, Australia.
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Li M, Xiong X, Xu B, Dickson C. Chinese Oncologists' Perspectives on Integrating AI into Clinical Practice: Cross-Sectional Survey Study. JMIR Form Res 2024; 8:e53918. [PMID: 38838307 DOI: 10.2196/53918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/21/2024] [Accepted: 04/03/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND The rapid development of artificial intelligence (AI) has brought significant interest to its potential applications in oncology. Although AI-powered tools are already being implemented in some Chinese hospitals, their integration into clinical practice raises several concerns for Chinese oncologists. OBJECTIVE This study aims to explore the concerns of Chinese oncologists regarding the integration of AI into clinical practice and to identify the factors influencing these concerns. METHODS A total of 228 Chinese oncologists participated in a cross-sectional web-based survey from April to June in 2023 in mainland China. The survey gauged their worries about AI with multiple-choice questions. The survey evaluated their views on the statements of "The impact of AI on the doctor-patient relationship" and "AI will replace doctors." The data were analyzed using descriptive statistics, and variate analyses were used to find correlations between the oncologists' backgrounds and their concerns. RESULTS The study revealed that the most prominent concerns were the potential for AI to mislead diagnosis and treatment (163/228, 71.5%); an overreliance on AI (162/228, 71%); data and algorithm bias (123/228, 54%); issues with data security and patient privacy (123/228, 54%); and a lag in the adaptation of laws, regulations, and policies in keeping up with AI's development (115/228, 50.4%). Oncologists with a bachelor's degree expressed heightened concerns related to data and algorithm bias (34/49, 69%; P=.03) and the lagging nature of legal, regulatory, and policy issues (32/49, 65%; P=.046). Regarding AI's impact on doctor-patient relationships, 53.1% (121/228) saw a positive impact, whereas 35.5% (81/228) found it difficult to judge, 9.2% (21/228) feared increased disputes, and 2.2% (5/228) believed that there is no impact. Although sex differences were not significant (P=.08), perceptions varied-male oncologists tended to be more positive than female oncologists (74/135, 54.8% vs 47/93, 50%). Oncologists with a bachelor's degree (26/49, 53%; P=.03) and experienced clinicians (≥21 years; 28/56, 50%; P=.054). found it the hardest to judge. Those with IT experience were significantly more positive (25/35, 71%) than those without (96/193, 49.7%; P=.02). Opinions regarding the possibility of AI replacing doctors were diverse, with 23.2% (53/228) strongly disagreeing, 14% (32/228) disagreeing, 29.8% (68/228) being neutral, 16.2% (37/228) agreeing, and 16.7% (38/228) strongly agreeing. There were no significant correlations with demographic and professional factors (all P>.05). CONCLUSIONS Addressing oncologists' concerns about AI requires collaborative efforts from policy makers, developers, health care professionals, and legal experts. Emphasizing transparency, human-centered design, bias mitigation, and education about AI's potential and limitations is crucial. Through close collaboration and a multidisciplinary strategy, AI can be effectively integrated into oncology, balancing benefits with ethical considerations and enhancing patient care.
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Affiliation(s)
- Ming Li
- Department of Health Policy Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - XiaoMin Xiong
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing University School of Medicine, Chongqing, China
| | - Bo Xu
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing University School of Medicine, Chongqing, China
- Department of Biochemistry and Molecular Biology, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Cancer Research Center, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Conan Dickson
- Department of Health Policy Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
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El-Tallawy SN, Pergolizzi JV, Vasiliu-Feltes I, Ahmed RS, LeQuang JK, El-Tallawy HN, Varrassi G, Nagiub MS. Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review. Pain Ther 2024; 13:293-317. [PMID: 38430433 PMCID: PMC11111436 DOI: 10.1007/s40122-024-00584-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 02/08/2024] [Indexed: 03/03/2024] Open
Abstract
Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision-making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions, this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment.
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Affiliation(s)
- Salah N El-Tallawy
- Anesthesia and Pain Department, College of Medicine, King Khalid University Hospital, King Saud University, Riyadh, Saudi Arabia.
- Anesthesia and Pain Department, Faculty of Medicine, Minia University & NCI, Cairo University, Giza, Egypt.
| | | | - Ingrid Vasiliu-Feltes
- Science, Entrepreneurship and Investments Institute, University of Miami, Miami, USA
| | - Rania S Ahmed
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
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Barlow J, Sragi Z, Rivera-Rivera G, Al-Awady A, Daşdöğen Ü, Courey MS, Kirke DN. The Use of Deep Learning Software in the Detection of Voice Disorders: A Systematic Review. Otolaryngol Head Neck Surg 2024; 170:1531-1543. [PMID: 38168017 DOI: 10.1002/ohn.636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 11/30/2023] [Accepted: 12/07/2023] [Indexed: 01/05/2024]
Abstract
OBJECTIVE To summarize the use of deep learning in the detection of voice disorders using acoustic and laryngoscopic input, compare specific neural networks in terms of accuracy, and assess their effectiveness compared to expert clinical visual examination. DATA SOURCES Embase, MEDLINE, and Cochrane Central. REVIEW METHODS Databases were screened through November 11, 2023 for relevant studies. The inclusion criteria required studies to utilize a specified deep learning method, use laryngoscopy or acoustic input, and measure accuracy of binary classification between healthy patients and those with voice disorders. RESULTS Thirty-four studies met the inclusion criteria, with 18 focusing on voice analysis, 15 on imaging analysis, and 1 both. Across the 18 acoustic studies, 21 programs were used for identification of organic and functional voice disorders. These technologies included 10 convolutional neural networks (CNNs), 6 multilayer perceptrons (MLPs), and 5 other neural networks. The binary classification systems yielded a mean accuracy of 89.0% overall, including 93.7% for MLP programs and 84.5% for CNNs. Among the 15 imaging analysis studies, a total of 23 programs were utilized, resulting in a mean accuracy of 91.3%. Specifically, the twenty CNNs achieved a mean accuracy of 92.6% compared to 83.0% for the 3 MLPs. CONCLUSION Deep learning models were shown to be highly accurate in the detection of voice pathology, with CNNs most effective for assessing laryngoscopy images and MLPs most effective for assessing acoustic input. While deep learning methods outperformed expert clinical exam in limited comparisons, further studies integrating external validation are necessary.
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Affiliation(s)
- Joshua Barlow
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Zara Sragi
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Gabriel Rivera-Rivera
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Abdurrahman Al-Awady
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Ümit Daşdöğen
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Mark S Courey
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Diana N Kirke
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
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Ying L, Li S, Chen C, Yang F, Li X, Chen Y, Ding Y, Chang G, Li J, Wang X. Screening/diagnosis of pediatric endocrine disorders through the artificial intelligence model in different language settings. Eur J Pediatr 2024; 183:2655-2661. [PMID: 38502320 PMCID: PMC11098926 DOI: 10.1007/s00431-024-05527-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/12/2024] [Accepted: 03/14/2024] [Indexed: 03/21/2024]
Abstract
This study is aimed at examining the impact of ChatGPT on pediatric endocrine and metabolic conditions, particularly in the areas of screening and diagnosis, in both Chinese and English modes. A 40-question questionnaire covering the four most common pediatric endocrine and metabolic conditions was posed to ChatGPT in both Chinese and English three times each. Six pediatric endocrinologists evaluated the responses. ChatGPT performed better when responding to questions in English, with an unreliable rate of 7.5% compared to 27.5% for Chinese questions, indicating a more consistent response pattern in English. Among the reliable questions, the answers were more comprehensive and satisfactory in the English mode. We also found disparities in ChatGPT's performance when interacting with different target groups and diseases, with improved performance for questions posed by clinicians in English and better performance for questions related to diabetes and overweight/obesity in Chinese for both clinicians and patients. Language comprehension, providing incomprehensive answers, and errors in key data were the main contributors to the low scores, according to reviewer feedback. CONCLUSION Despite these limitations, as ChatGPT continues to evolve and expand its network, it has significant potential as a practical and effective tool for clinical diagnosis and treatment. WHAT IS KNOWN • The deep learning-based large-language model ChatGPT holds great promise for improving clinical practice for both physicians and patients and has the potential to increase the speed and accuracy of disease screening and diagnosis, as well as enhance the overall efficiency of the medical process. However, the reliability and appropriateness of AI model responses in specific field remains unclear. • This study focused on the reliability and appropriateness of AI model responses to straightforward and fundamental questions related to the four most prevalent pediatric endocrine and metabolic disorders, for both healthcare providers and patients, in different language scenarios. WHAT IS NEW • The AI model performed better when responding to questions in English, with more consistent, as well as more comprehensive and satisfactory responses. In addition, we also found disparities in ChatGPT's performance when interacting with different target groups and different diseases. • Despite these limitations, as ChatGPT continues to evolve and expand its network, it has significant potential as a practical and effective tool for clinical diagnosis and treatment.
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Affiliation(s)
- Lingwen Ying
- Department of Endocrinology and Metabolism, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Sichen Li
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China
- National Center for Neurological Disorders, Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery, Shanghai, 200040, China
| | - Chunyang Chen
- Faculty of Information Technology, Monash University, Clayton, VIC, 3800, Australia
| | - Fan Yang
- Department of Endocrinology and Metabolism, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Xin Li
- Department of Endocrinology and Metabolism, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yao Chen
- Department of Endocrinology and Metabolism, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yu Ding
- Department of Endocrinology and Metabolism, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Guoying Chang
- Department of Endocrinology and Metabolism, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
| | - Juan Li
- Department of Endocrinology and Metabolism, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Xiumin Wang
- Department of Endocrinology and Metabolism, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
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Ye J, Woods D, Jordan N, Starren J. The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2024; 2024:459-467. [PMID: 38827061 PMCID: PMC11141850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
This narrative review aims to identify and understand the role of artificial intelligence in the application of integrated electronic health records (EHRs) and patient-generated health data (PGHD) in clinical decision support. We focused on integrated data that combined PGHD and EHR data, and we investigated the role of artificial intelligence (AI) in the application. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search articles in six databases: PubMed, Embase, Web of Science, Scopus, ACM Digital Library, and IEEE Computer Society Digital Library. In addition, we also synthesized seminal sources, including other systematic reviews, reports, and white papers, to inform the context, history, and development of this field. Twenty-six publications met the review criteria after screening. The EHR-integrated PGHD introduces benefits to health care, including empowering patients and families to engage via shared decision-making, improving the patient-provider relationship, and reducing the time and cost of clinical visits. AI's roles include cleaning and management of heterogeneous datasets, assisting in identifying dynamic patterns to improve clinical care processes, and providing more sophisticated algorithms to better predict outcomes and propose precise recommendations based on the integrated data. Challenges mainly stem from the large volume of integrated data, data standards, data exchange and interoperability, security and privacy, interpretation, and meaningful use. The use of PGHD in health care is at a promising stage but needs further work for widespread adoption and seamless integration into health care systems. AI-driven, EHR-integrated PGHD systems can greatly improve clinicians' abilities to diagnose patients' health issues, classify risks at the patient level by drawing on the power of integrated data, and provide much-needed support to clinics and hospitals. With EHR-integrated PGHD, AI can help transform health care by improving diagnosis, treatment, and the delivery of clinical care, thus improving clinical decision support.
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Affiliation(s)
- Jiancheng Ye
- Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Donna Woods
- Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Neil Jordan
- Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Justin Starren
- Feinberg School of Medicine, Northwestern University, Chicago, USA
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Zondag AGM, Rozestraten R, Grimmelikhuijsen SG, Jongsma KR, van Solinge WW, Bots ML, Vernooij RWM, Haitjema S. The Effect of Artificial Intelligence on Patient-Physician Trust: Cross-Sectional Vignette Study. J Med Internet Res 2024; 26:e50853. [PMID: 38805702 DOI: 10.2196/50853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 03/21/2024] [Accepted: 04/16/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND Clinical decision support systems (CDSSs) based on routine care data, using artificial intelligence (AI), are increasingly being developed. Previous studies focused largely on the technical aspects of using AI, but the acceptability of these technologies by patients remains unclear. OBJECTIVE We aimed to investigate whether patient-physician trust is affected when medical decision-making is supported by a CDSS. METHODS We conducted a vignette study among the patient panel (N=860) of the University Medical Center Utrecht, the Netherlands. Patients were randomly assigned into 4 groups-either the intervention or control groups of the high-risk or low-risk cases. In both the high-risk and low-risk case groups, a physician made a treatment decision with (intervention groups) or without (control groups) the support of a CDSS. Using a questionnaire with a 7-point Likert scale, with 1 indicating "strongly disagree" and 7 indicating "strongly agree," we collected data on patient-physician trust in 3 dimensions: competence, integrity, and benevolence. We assessed differences in patient-physician trust between the control and intervention groups per case using Mann-Whitney U tests and potential effect modification by the participant's sex, age, education level, general trust in health care, and general trust in technology using multivariate analyses of (co)variance. RESULTS In total, 398 patients participated. In the high-risk case, median perceived competence and integrity were lower in the intervention group compared to the control group but not statistically significant (5.8 vs 5.6; P=.16 and 6.3 vs 6.0; P=.06, respectively). However, the effect of a CDSS application on the perceived competence of the physician depended on the participant's sex (P=.03). Although no between-group differences were found in men, in women, the perception of the physician's competence and integrity was significantly lower in the intervention compared to the control group (P=.009 and P=.01, respectively). In the low-risk case, no differences in trust between the groups were found. However, increased trust in technology positively influenced the perceived benevolence and integrity in the low-risk case (P=.009 and P=.04, respectively). CONCLUSIONS We found that, in general, patient-physician trust was high. However, our findings indicate a potentially negative effect of AI applications on the patient-physician relationship, especially among women and in high-risk situations. Trust in technology, in general, might increase the likelihood of embracing the use of CDSSs by treating professionals.
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Affiliation(s)
- Anna G M Zondag
- Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Raoul Rozestraten
- Utrecht University School of Governance, Utrecht University, Utrecht, Netherlands
| | | | - Karin R Jongsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Wouter W van Solinge
- Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Michiel L Bots
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Robin W M Vernooij
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht, Netherlands
| | - Saskia Haitjema
- Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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Farah L, Borget I, Martelli N, Vallee A. Suitability of the Current Health Technology Assessment of Innovative Artificial Intelligence-Based Medical Devices: Scoping Literature Review. J Med Internet Res 2024; 26:e51514. [PMID: 38739911 PMCID: PMC11130781 DOI: 10.2196/51514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/17/2023] [Accepted: 12/28/2023] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI)-based medical devices have garnered attention due to their ability to revolutionize medicine. Their health technology assessment framework is lacking. OBJECTIVE This study aims to analyze the suitability of each health technology assessment (HTA) domain for the assessment of AI-based medical devices. METHODS We conducted a scoping literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We searched databases (PubMed, Embase, and Cochrane Library), gray literature, and HTA agency websites. RESULTS A total of 10.1% (78/775) of the references were included. Data quality and integration are vital aspects to consider when describing and assessing the technical characteristics of AI-based medical devices during an HTA process. When it comes to implementing specialized HTA for AI-based medical devices, several practical challenges and potential barriers could be highlighted and should be taken into account (AI technological evolution timeline, data requirements, complexity and transparency, clinical validation and safety requirements, regulatory and ethical considerations, and economic evaluation). CONCLUSIONS The adaptation of the HTA process through a methodological framework for AI-based medical devices enhances the comparability of results across different evaluations and jurisdictions. By defining the necessary expertise, the framework supports the development of a skilled workforce capable of conducting robust and reliable HTAs of AI-based medical devices. A comprehensive adapted HTA framework for AI-based medical devices can provide valuable insights into the effectiveness, cost-effectiveness, and societal impact of AI-based medical devices, guiding their responsible implementation and maximizing their benefits for patients and health care systems.
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Affiliation(s)
- Line Farah
- Innovation Center for Medical Devices Department, Foch Hospital, Suresnes, France
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
| | - Isabelle Borget
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
- Department of Biostatistics and Epidemiology, Gustave Roussy, University Paris-Saclay, Villejuif, France
- Oncostat U1018, Inserm, Équipe Labellisée Ligue Contre le Cancer, University Paris-Saclay, Villejuif, France
| | - Nicolas Martelli
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
- Pharmacy Department, Georges Pompidou European Hospital, Paris, France
| | - Alexandre Vallee
- Department of Epidemiology and Public Health, Foch Hospital, Suresnes, France
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Li W, Shang N, Zhang Z, Li Y, Li X, Zheng X. Development and validation of a machine learning model to improve precision prediction for irrational prescriptions in orthopedic perioperative patients. Expert Opin Drug Saf 2024:1-11. [PMID: 38698685 DOI: 10.1080/14740338.2024.2348569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 03/19/2024] [Indexed: 05/05/2024]
Abstract
OBJECTIVE Our objective was to develop a machine learning model capable of predicting irrational medical prescriptions precisely within orthopedic perioperative patients. METHODS A dataset comprising 3047 instances of suspected irrational medication prescriptions was collected from a sample of 1318 orthopedic perioperative patients from April 2019 to March 2022. Four machine learning models were employed to forecast irrational prescriptions, following which, the performance of each model was meticulously assessed. Subsequently, a thorough variable importance analysis was conducted on the model that performed the best predictive capabilities. Thereafter, the efficacy of integrating this optimal model into the existing audit prescription process was rigorously evaluated. RESULTS Of the models utilized in this study, the RF model yielded the highest AUC of 92%, whereas the NB model presented the lowest AUC of 68%. Also, the RF model boasted the most robust performance in terms of PPV, reaching 82.4%, and NPV, reaching 86.6%. The ANN and the XGBoost model were neck and neck, with the ANN slightly edging out with a higher PPV of 95.9%, while the XGBoost model boasted an impressive NPV of 98.2%. The RF model singled out the following five factors as the most influential in predicting irrational prescriptions: the type of drug, the type of surgery, the number of comorbidities, the date of surgery after hospitalization, as well as the associated hospital and drug costs. CONCLUSION The RF model showcased significantly high level of proficiency in predicting irrational prescriptions among orthopedic perioperative patients, outperforming other models by a considerable margin. It effectively enhanced the efficiency of pharmacist interventions, displaying outstanding performance in assisting pharmacists to intervene with irrational prescriptions.
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Affiliation(s)
- Weipeng Li
- School of Pharmacy, Shanxi Medical University, Taiyuan, Shanxi Province, P.R. China
| | - Nan Shang
- Department of Pharmacy, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, P.R. China
| | - Zhiqi Zhang
- Department of Pharmacy, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, P.R. China
| | - Yun Li
- Department of Pharmacy, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, P.R. China
| | - Xianlin Li
- School of Pharmacy, Shanxi Medical University, Taiyuan, Shanxi Province, P.R. China
| | - Xiaojun Zheng
- Department of Pharmacy, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, P.R. China
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Nazarizadeh A, Banirostam T, Biglari T, Kalantarhormozi M, Chichagi F, Behnoush AH, Habibi MA, Shahidi R. Integrated neural network and evolutionary algorithm approach for liver fibrosis staging: Can artificial intelligence reduce patient costs? JGH Open 2024; 8:e13075. [PMID: 38725944 PMCID: PMC11079785 DOI: 10.1002/jgh3.13075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 10/28/2023] [Accepted: 04/18/2024] [Indexed: 05/12/2024]
Abstract
Background and Aim Staging liver fibrosis is important, and liver biopsy is the gold standard diagnostic tool. We aim to design and evaluate an artificial neural network (ANN) method by taking advantage of the Teaching Learning-Based Optimization (TLBO) algorithm for the prediction of liver fibrosis stage in blood donors and hepatitis C patients. Methods We propose a method based on a selection of machine learning classification methods including multilayer perceptron (MLP) neural network, Naive Bayesian (NB), decision tree, and deep learning. Initially, the synthetic minority oversampling technique (SMOTE) is performed to address the imbalance in the dataset. Afterward, the integration of MLP and TLBO is implemented. Results We propose a novel algorithm that reduces the number of required patient features to seven inputs. The accuracy of MLP using 12 features is 0.903, while that of the proposed MLP with TLBO is 0.891. Besides, the diagnostic accuracy of all methods, except the model designed with the Bayesian network, increases when the SMOTE balancer is applied. Conclusion The decision tree-based deep learning methods show the highest levels of accuracy with 12 features. Interestingly, with the use of TLBO and seven features, MLP reached an accuracy rate of 0.891, which is quite satisfactory when compared with those of similar studies. The proposed model provides high diagnostic accuracy, while reducing the required number of properties from the samples. The results of our study show that the recruited algorithm of our study is more straightforward, with a smaller number of required properties and similar accuracy.
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Affiliation(s)
- Ali Nazarizadeh
- Department of Computer EngineeringCentral Tehran Branch, Islamic Azad UniversityTehranIran
| | - Touraj Banirostam
- Department of Computer EngineeringCentral Tehran Branch, Islamic Azad UniversityTehranIran
| | - Taraneh Biglari
- Department of Computer EngineeringCentral Tehran Branch, Islamic Azad UniversityTehranIran
| | - Mohammadreza Kalantarhormozi
- The Persian Gulf Tropical Medicine Research CenterThe Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical SciencesBushehrIran
| | - Fatemeh Chichagi
- Students' Scientific Research Center (SSRC)Tehran University of Medical SciencesTehranIran
| | - Amir H Behnoush
- Non–Communicable Diseases Research CenterEndocrinology and Metabolism Population Sciences Institute, Tehran University of Medical SciencesTehranIran
| | - Mohammad A Habibi
- Clinical Research Development CenterShahid Beheshti Hospital, Qom University of Medical SciencesQomIran
| | - Ramin Shahidi
- School of MedicineBushehr University of Medical SciencesBushehrIran
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11
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Vaidya YP, Shumway SJ. Artificial intelligence: The future of cardiothoracic surgery. J Thorac Cardiovasc Surg 2024:S0022-5223(24)00371-4. [PMID: 38685465 DOI: 10.1016/j.jtcvs.2024.04.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/02/2024]
Affiliation(s)
- Yash Pradeep Vaidya
- Department of Cardiothoracic Surgery, University of Minnesota, Minneapolis, Minn.
| | - Sara Jane Shumway
- Department of Cardiothoracic Surgery, University of Minnesota, Minneapolis, Minn
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12
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Katwaroo AR, Adesh VS, Lowtan A, Umakanthan S. The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine. Postgrad Med J 2024; 100:289-296. [PMID: 38159301 DOI: 10.1093/postmj/qgad135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 12/02/2023] [Indexed: 01/03/2024]
Abstract
In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.
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Affiliation(s)
- Arun Rabindra Katwaroo
- Department of Medicine, Trinidad Institute of Medical Technology, St Augustine, Trinidad and Tobago
| | | | - Amrita Lowtan
- Department of Preclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St. Augustine, Trinidad and Tobago
| | - Srikanth Umakanthan
- Department of Paraclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St. Augustine, Trinidad and Tobago
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Kernberg A, Gold JA, Mohan V. Using ChatGPT-4 to Create Structured Medical Notes From Audio Recordings of Physician-Patient Encounters: Comparative Study. J Med Internet Res 2024; 26:e54419. [PMID: 38648636 DOI: 10.2196/54419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/20/2024] [Accepted: 03/10/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Medical documentation plays a crucial role in clinical practice, facilitating accurate patient management and communication among health care professionals. However, inaccuracies in medical notes can lead to miscommunication and diagnostic errors. Additionally, the demands of documentation contribute to physician burnout. Although intermediaries like medical scribes and speech recognition software have been used to ease this burden, they have limitations in terms of accuracy and addressing provider-specific metrics. The integration of ambient artificial intelligence (AI)-powered solutions offers a promising way to improve documentation while fitting seamlessly into existing workflows. OBJECTIVE This study aims to assess the accuracy and quality of Subjective, Objective, Assessment, and Plan (SOAP) notes generated by ChatGPT-4, an AI model, using established transcripts of History and Physical Examination as the gold standard. We seek to identify potential errors and evaluate the model's performance across different categories. METHODS We conducted simulated patient-provider encounters representing various ambulatory specialties and transcribed the audio files. Key reportable elements were identified, and ChatGPT-4 was used to generate SOAP notes based on these transcripts. Three versions of each note were created and compared to the gold standard via chart review; errors generated from the comparison were categorized as omissions, incorrect information, or additions. We compared the accuracy of data elements across versions, transcript length, and data categories. Additionally, we assessed note quality using the Physician Documentation Quality Instrument (PDQI) scoring system. RESULTS Although ChatGPT-4 consistently generated SOAP-style notes, there were, on average, 23.6 errors per clinical case, with errors of omission (86%) being the most common, followed by addition errors (10.5%) and inclusion of incorrect facts (3.2%). There was significant variance between replicates of the same case, with only 52.9% of data elements reported correctly across all 3 replicates. The accuracy of data elements varied across cases, with the highest accuracy observed in the "Objective" section. Consequently, the measure of note quality, assessed by PDQI, demonstrated intra- and intercase variance. Finally, the accuracy of ChatGPT-4 was inversely correlated to both the transcript length (P=.05) and the number of scorable data elements (P=.05). CONCLUSIONS Our study reveals substantial variability in errors, accuracy, and note quality generated by ChatGPT-4. Errors were not limited to specific sections, and the inconsistency in error types across replicates complicated predictability. Transcript length and data complexity were inversely correlated with note accuracy, raising concerns about the model's effectiveness in handling complex medical cases. The quality and reliability of clinical notes produced by ChatGPT-4 do not meet the standards required for clinical use. Although AI holds promise in health care, caution should be exercised before widespread adoption. Further research is needed to address accuracy, variability, and potential errors. ChatGPT-4, while valuable in various applications, should not be considered a safe alternative to human-generated clinical documentation at this time.
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Affiliation(s)
- Annessa Kernberg
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Sciences University, Portland, OR, United States
| | - Jeffrey A Gold
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Sciences University, Portland, OR, United States
| | - Vishnu Mohan
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Sciences University, Portland, OR, United States
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14
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Wimbarti S, Kairupan BHR, Tallei TE. Critical review of self-diagnosis of mental health conditions using artificial intelligence. Int J Ment Health Nurs 2024; 33:344-358. [PMID: 38345132 DOI: 10.1111/inm.13303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 03/10/2024]
Abstract
The advent of artificial intelligence (AI) has revolutionised various aspects of our lives, including mental health nursing. AI-driven tools and applications have provided a convenient and accessible means for individuals to assess their mental well-being within the confines of their homes. Nonetheless, the widespread trend of self-diagnosing mental health conditions through AI poses considerable risks. This review article examines the perils associated with relying on AI for self-diagnosis in mental health, highlighting the constraints and possible adverse outcomes that can arise from such practices. It delves into the ethical, psychological, and social implications, underscoring the vital role of mental health professionals, including psychologists, psychiatrists, and nursing specialists, in providing professional assistance and guidance. This article aims to highlight the importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI-driven self-diagnosis.
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Affiliation(s)
- Supra Wimbarti
- Faculty of Psychology, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - B H Ralph Kairupan
- Department of Psychiatry, Faculty of Medicine, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
| | - Trina Ekawati Tallei
- Department of Biology, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
- Department of Biology, Faculty of Medicine, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
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15
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Al-Anazi S, Al-Omari A, Alanazi S, Marar A, Asad M, Alawaji F, Alwateid S. Artificial intelligence in respiratory care: Current scenario and future perspective. Ann Thorac Med 2024; 19:117-130. [PMID: 38766378 PMCID: PMC11100474 DOI: 10.4103/atm.atm_192_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field. METHODS A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making. RESULTS The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies. CONCLUSION By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices. IMPROVEMENTS Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care.
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Affiliation(s)
- Saad Al-Anazi
- Lead Clincial Appliaction AzeerTrade (Lowenstein Medical Company), Riyadh, Saudi Arabia
| | - Awad Al-Omari
- Department of Intensive Care, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Safug Alanazi
- Intensivist, Al Hammadi Hospital, Riyadh, Saudi Arabia
| | - Aqeelah Marar
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Mohammed Asad
- Department of Emergency, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Fadi Alawaji
- Ar Rass General Hospital, Qassim Health Cluster, Senior Laboratory Specialist, Rass Region, Qassim City, Saudi Arabia
| | - Salman Alwateid
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
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16
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Breeding T, Martinez B, Patel H, Nasef H, Arif H, Nakayama D, Elkbuli A. The Utilization of ChatGPT in Reshaping Future Medical Education and Learning Perspectives: A Curse or a Blessing? Am Surg 2024; 90:560-566. [PMID: 37309705 DOI: 10.1177/00031348231180950] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
BACKGROUND ChatGPT has substantial potential to revolutionize medical education. We aim to assess how medical students and laypeople evaluate information produced by ChatGPT compared to an evidence-based resource on the diagnosis and management of 5 common surgical conditions. METHODS A 60-question anonymous online survey was distributed to third- and fourth-year U.S. medical students and laypeople to evaluate articles produced by ChatGPT and an evidence-based source on clarity, relevance, reliability, validity, organization, and comprehensiveness. Participants received 2 blinded articles, 1 from each source, for each surgical condition. Paired-sample t-tests were used to compare ratings between the 2 sources. RESULTS Of 56 survey participants, 50.9% (n = 28) were U.S. medical students and 49.1% (n = 27) were from the general population. Medical students reported that ChatGPT articles displayed significantly more clarity (appendicitis: 4.39 vs 3.89, P = .020; diverticulitis: 4.54 vs 3.68, P < .001; SBO 4.43 vs 3.79, P = .003; GI bleed: 4.36 vs 3.93, P = .020) and better organization (diverticulitis: 4.36 vs 3.68, P = .021; SBO: 4.39 vs 3.82, P = .033) than the evidence-based source. However, for all 5 conditions, medical students found evidence-based passages to be more comprehensive than ChatGPT articles (cholecystitis: 4.04 vs 3.36, P = .009; appendicitis: 4.07 vs 3.36, P = .015; diverticulitis: 4.07 vs 3.36, P = .015; small bowel obstruction: 4.11 vs 3.54, P = .030; upper GI bleed: 4.11 vs 3.29, P = .003). CONCLUSION Medical students perceived ChatGPT articles to be clearer and better organized than evidence-based sources on the pathogenesis, diagnosis, and management of 5 common surgical pathologies. However, evidence-based articles were rated as significantly more comprehensive.
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Affiliation(s)
- Tessa Breeding
- Kiran Patel College of Allopathic Medicine, NOVA Southeastern University, Fort Lauderdale, FL, USA
| | - Brian Martinez
- Kiran Patel College of Allopathic Medicine, NOVA Southeastern University, Fort Lauderdale, FL, USA
| | - Heli Patel
- Kiran Patel College of Allopathic Medicine, NOVA Southeastern University, Fort Lauderdale, FL, USA
| | - Hazem Nasef
- Kiran Patel College of Allopathic Medicine, NOVA Southeastern University, Fort Lauderdale, FL, USA
| | - Hasan Arif
- Kiran Patel College of Allopathic Medicine, NOVA Southeastern University, Fort Lauderdale, FL, USA
| | - Don Nakayama
- Mercer University School of Medicine, Columbus, GA, USA
- Department of Pediatric Surgery, Piedmont Columbus Regional Hospital, Piedmont, GA, USA
| | - Adel Elkbuli
- Department of Surgery, Division of Trauma and Surgical Critical Care, Orlando Regional Medical Center, Orlando, FL, USA
- Department of Surgical Education, Orlando Regional Medical Center, Orlando, FL, USA
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17
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Anandhi G, Iyapparaja M. Photocatalytic degradation of drugs and dyes using a maching learning approach. RSC Adv 2024; 14:9003-9019. [PMID: 38500628 PMCID: PMC10945304 DOI: 10.1039/d4ra00711e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 03/02/2024] [Indexed: 03/20/2024] Open
Abstract
The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior of organic pollutants during catalytic degradation. With the increasing quantity of waste generated, these models are critical for reinforcing the efficiency of wastewater treatment strategies. The application of machine-learning techniques in recent years has notably improved predictive models for waste management, which are essential for mitigating the impact of toxic commercial waste on global water supply. Organic contaminants, dyes, pesticides, surfactants, petroleum by-products, and prescription drugs pose risks to human health. Because traditional techniques face challenges in ensuring water quality, modern strategies are vital. Machine learning has emerged as a valuable tool for predicting the photocatalytic degradation of medicinal drugs and dyes, providing a promising avenue for addressing urgent demands in removing organic pollutants from wastewater. This research investigates the synergistic application of photocatalysis and machine learning for pollutant degradation, showcasing a sustainable solution with promising effects on environmental remediation and computational efficiency. This study contributes to green chemistry by providing a clever framework for addressing present-day water pollution challenges and achieving era-driven answers.
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Affiliation(s)
- Ganesan Anandhi
- Department of Smart Computing, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology Vellore 632014 Tamil Nadu India
| | - M Iyapparaja
- Department of Smart Computing, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology Vellore 632014 Tamil Nadu India
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18
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Benani A. [History and vision of artificial intelligence]. Med Sci (Paris) 2024; 40:283-286. [PMID: 38520104 DOI: 10.1051/medsci/2024022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2024] Open
Affiliation(s)
- Alaedine Benani
- Service de médecine vasculaire, hôpital européen Georges Pompidou (HEGP), AP-HP, Université Paris-Cité, Paris, France
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19
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Ko J, Song J, Choi N, Kim HN. Patient-Derived Microphysiological Systems for Precision Medicine. Adv Healthc Mater 2024; 13:e2303161. [PMID: 38010253 DOI: 10.1002/adhm.202303161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Indexed: 11/29/2023]
Abstract
Patient-derived microphysiological systems (P-MPS) have emerged as powerful tools in precision medicine that provide valuable insight into individual patient characteristics. This review discusses the development of P-MPS as an integration of patient-derived samples, including patient-derived cells, organoids, and induced pluripotent stem cells, into well-defined MPSs. Emphasizing the necessity of P-MPS development, its significance as a nonclinical assessment approach that bridges the gap between traditional in vitro models and clinical outcomes is highlighted. Additionally, guidance is provided for engineering approaches to develop microfluidic devices and high-content analysis for P-MPSs, enabling high biological relevance and high-throughput experimentation. The practical implications of the P-MPS are further examined by exploring the clinically relevant outcomes obtained from various types of patient-derived samples. The construction and analysis of these diverse samples within the P-MPS have resulted in physiologically relevant data, paving the way for the development of personalized treatment strategies. This study describes the significance of the P-MPS in precision medicine, as well as its unique capacity to offer valuable insights into individual patient characteristics.
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Affiliation(s)
- Jihoon Ko
- Department of BioNano Technology, Gachon University, Seongnam-si, Gyeonggi-do, 13120, Republic of Korea
| | - Jiyoung Song
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Nakwon Choi
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
- Division of Bio-Medical Science & Technology, KIST School, Seoul, 02792, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, 02841, Republic of Korea
| | - Hong Nam Kim
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
- Division of Bio-Medical Science & Technology, KIST School, Seoul, 02792, Republic of Korea
- School of Mechanical Engineering, Yonsei University, Seoul, 03722, Republic of Korea
- Yonsei-KIST Convergence Research Institute, Yonsei University, Seoul, 03722, Republic of Korea
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20
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Gala D, Behl H, Shah M, Makaryus AN. The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature. Healthcare (Basel) 2024; 12:481. [PMID: 38391856 PMCID: PMC10887513 DOI: 10.3390/healthcare12040481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024] Open
Abstract
Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.
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Affiliation(s)
- Dhir Gala
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Haditya Behl
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Mili Shah
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Amgad N Makaryus
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, 500 Hofstra Blvd., Hempstead, NY 11549, USA
- Department of Cardiology, Nassau University Medical Center, Hempstead, NY 11554, USA
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Brandão M, Mendes F, Martins M, Cardoso P, Macedo G, Mascarenhas T, Mascarenhas Saraiva M. Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology. J Clin Med 2024; 13:1061. [PMID: 38398374 PMCID: PMC10889757 DOI: 10.3390/jcm13041061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.
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Affiliation(s)
- Marta Brandão
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Teresa Mascarenhas
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Obstetrics and Gynecology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Mascarenhas Saraiva
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
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Weidener L, Fischer M. Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education. JMIR MEDICAL EDUCATION 2024; 10:e55368. [PMID: 38285931 PMCID: PMC10891487 DOI: 10.2196/55368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/02/2024] [Accepted: 01/29/2024] [Indexed: 01/31/2024]
Abstract
The use of artificial intelligence (AI) in medicine, potentially leading to substantial advancements such as improved diagnostics, has been of increasing scientific and societal interest in recent years. However, the use of AI raises new ethical challenges, such as an increased risk of bias and potential discrimination against patients, as well as misdiagnoses potentially leading to over- or underdiagnosis with substantial consequences for patients. Recognizing these challenges, current research underscores the importance of integrating AI ethics into medical education. This viewpoint paper aims to introduce a comprehensive set of ethical principles for teaching AI ethics in medical education. This dynamic and principle-based approach is designed to be adaptive and comprehensive, addressing not only the current but also emerging ethical challenges associated with the use of AI in medicine. This study conducts a theoretical analysis of the current academic discourse on AI ethics in medical education, identifying potential gaps and limitations. The inherent interconnectivity and interdisciplinary nature of these anticipated challenges are illustrated through a focused discussion on "informed consent" in the context of AI in medicine and medical education. This paper proposes a principle-based approach to AI ethics education, building on the 4 principles of medical ethics-autonomy, beneficence, nonmaleficence, and justice-and extending them by integrating 3 public health ethics principles-efficiency, common good orientation, and proportionality. The principle-based approach to teaching AI ethics in medical education proposed in this study offers a foundational framework for addressing the anticipated ethical challenges of using AI in medicine, recommended in the current academic discourse. By incorporating the 3 principles of public health ethics, this principle-based approach ensures that medical ethics education remains relevant and responsive to the dynamic landscape of AI integration in medicine. As the advancement of AI technologies in medicine is expected to increase, medical ethics education must adapt and evolve accordingly. The proposed principle-based approach for teaching AI ethics in medical education provides an important foundation to ensure that future medical professionals are not only aware of the ethical dimensions of AI in medicine but also equipped to make informed ethical decisions in their practice. Future research is required to develop problem-based and competency-oriented learning objectives and educational content for the proposed principle-based approach to teaching AI ethics in medical education.
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Affiliation(s)
- Lukas Weidener
- UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
| | - Michael Fischer
- UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
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Yang YY, Ho MY, Tai CH, Wu RM, Kuo MC, Tseng YJ. FastEval Parkinsonism: an instant deep learning-assisted video-based online system for Parkinsonian motor symptom evaluation. NPJ Digit Med 2024; 7:31. [PMID: 38332372 PMCID: PMC10853559 DOI: 10.1038/s41746-024-01022-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 01/18/2024] [Indexed: 02/10/2024] Open
Abstract
The Motor Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is designed to assess bradykinesia, the cardinal symptoms of Parkinson's disease (PD). However, it cannot capture the all-day variability of bradykinesia outside the clinical environment. Here, we introduce FastEval Parkinsonism ( https://fastevalp.cmdm.tw/ ), a deep learning-driven video-based system, providing users to capture keypoints, estimate the severity, and summarize in a report. Leveraging 840 finger-tapping videos from 186 individuals (103 patients with Parkinson's disease (PD), 24 participants with atypical parkinsonism (APD), 12 elderly with mild parkinsonism signs (MPS), and 47 healthy controls (HCs)), we employ a dilated convolution neural network with two data augmentation techniques. Our model achieves acceptable accuracies (AAC) of 88.0% and 81.5%. The frequency-intensity (FI) value of thumb-index finger distance was indicated as a pivotal hand parameter to quantify the performance. Our model also shows the usability for multi-angle videos, tested in an external database enrolling over 300 PD patients.
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Affiliation(s)
- Yu-Yuan Yang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC
| | - Ming-Yang Ho
- Department of Computer Science and Information Engineering, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC
| | - Chung-Hwei Tai
- Department of Neurology, National Taiwan University Hospital, No. 1 Changde St., Zhongzheng Dist., Taipei City, 100229, Taiwan, ROC
| | - Ruey-Meei Wu
- Department of Medicine, National Taiwan University Cancer Center, No. 57, Lane 155, Sec. 3, Keelung Rd., Da'an Dist., Taipei City, 106, Taiwan, ROC
| | - Ming-Che Kuo
- Department of Neurology, National Taiwan University Hospital, No. 1 Changde St., Zhongzheng Dist., Taipei City, 100229, Taiwan, ROC.
- Department of Medicine, National Taiwan University Cancer Center, No. 57, Lane 155, Sec. 3, Keelung Rd., Da'an Dist., Taipei City, 106, Taiwan, ROC.
| | - Yufeng Jane Tseng
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC.
- Department of Computer Science and Information Engineering, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC.
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Aamir A, Iqbal A, Jawed F, Ashfaque F, Hafsa H, Anas Z, Oduoye MO, Basit A, Ahmed S, Abdul Rauf S, Khan M, Mansoor T. Exploring the current and prospective role of artificial intelligence in disease diagnosis. Ann Med Surg (Lond) 2024; 86:943-949. [PMID: 38333305 PMCID: PMC10849462 DOI: 10.1097/ms9.0000000000001700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/28/2023] [Indexed: 02/10/2024] Open
Abstract
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems, providing assistance in a variety of patient care and health systems. The aim of this review is to contribute valuable insights to the ongoing discourse on the transformative potential of AI in healthcare, providing a nuanced understanding of its current applications, future possibilities, and associated challenges. The authors conducted a literature search on the current role of AI in disease diagnosis and its possible future applications using PubMed, Google Scholar, and ResearchGate within 10 years. Our investigation revealed that AI, encompassing machine-learning and deep-learning techniques, has become integral to healthcare, facilitating immediate access to evidence-based guidelines, the latest medical literature, and tools for generating differential diagnoses. However, our research also acknowledges the limitations of current AI methodologies in disease diagnosis and explores uncertainties and obstacles associated with the complete integration of AI into clinical practice. This review has highlighted the critical significance of integrating AI into the medical healthcare framework and meticulously examined the evolutionary trajectory of healthcare-oriented AI from its inception, delving into the current state of development and projecting the extent of reliance on AI in the future. The authors have found that central to this study is the exploration of how the strategic integration of AI can accelerate the diagnostic process, heighten diagnostic accuracy, and enhance overall operational efficiency, concurrently relieving the burdens faced by healthcare practitioners.
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Affiliation(s)
- Ali Aamir
- Department of Medicine, Dow University of Health Sciences
| | - Arham Iqbal
- Department of Medicine, Dow International Medical College, Karachi, Pakistan
| | - Fareeha Jawed
- Department of Medicine, Dow University of Health Sciences
| | - Faiza Ashfaque
- Department of Medicine, Dow University of Health Sciences
| | - Hafiza Hafsa
- Department of Medicine, Dow University of Health Sciences
| | - Zahra Anas
- Department of Medicine, Dow University of Health Sciences
| | - Malik Olatunde Oduoye
- Department of Research, Medical Research Circle, Bukavu, Democratic Republic of Congo
| | - Abdul Basit
- Department of Medicine, Dow University of Health Sciences
| | - Shaheer Ahmed
- Department of Medicine, Dow University of Health Sciences
| | | | - Mushkbar Khan
- Liaquat National Hospital and Medical College, Pakistan
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Allen MR, Webb S, Mandvi A, Frieden M, Tai-Seale M, Kallenberg G. Navigating the doctor-patient-AI relationship - a mixed-methods study of physician attitudes toward artificial intelligence in primary care. BMC PRIMARY CARE 2024; 25:42. [PMID: 38281026 PMCID: PMC10821550 DOI: 10.1186/s12875-024-02282-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 01/19/2024] [Indexed: 01/29/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is a rapidly advancing field that is beginning to enter the practice of medicine. Primary care is a cornerstone of medicine and deals with challenges such as physician shortage and burnout which impact patient care. AI and its application via digital health is increasingly presented as a possible solution. However, there is a scarcity of research focusing on primary care physician (PCP) attitudes toward AI. This study examines PCP views on AI in primary care. We explore its potential impact on topics pertinent to primary care such as the doctor-patient relationship and clinical workflow. By doing so, we aim to inform primary care stakeholders to encourage successful, equitable uptake of future AI tools. Our study is the first to our knowledge to explore PCP attitudes using specific primary care AI use cases rather than discussing AI in medicine in general terms. METHODS From June to August 2023, we conducted a survey among 47 primary care physicians affiliated with a large academic health system in Southern California. The survey quantified attitudes toward AI in general as well as concerning two specific AI use cases. Additionally, we conducted interviews with 15 survey respondents. RESULTS Our findings suggest that PCPs have largely positive views of AI. However, attitudes often hinged on the context of adoption. While some concerns reported by PCPs regarding AI in primary care focused on technology (accuracy, safety, bias), many focused on people-and-process factors (workflow, equity, reimbursement, doctor-patient relationship). CONCLUSION Our study offers nuanced insights into PCP attitudes towards AI in primary care and highlights the need for primary care stakeholder alignment on key issues raised by PCPs. AI initiatives that fail to address both the technological and people-and-process concerns raised by PCPs may struggle to make an impact.
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Affiliation(s)
- Matthew R Allen
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA.
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA, 92093, USA.
| | - Sophie Webb
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ammar Mandvi
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Marshall Frieden
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ming Tai-Seale
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Gene Kallenberg
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
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Kong X, Tao X, Li L, Zhao X, Ren J, Yang S, Chen X, Xiang H, Wu G, Li Y, Dong D. Global trends and partial forecast of adverse effects of medical treatment from 1990 to 2019: an epidemiological analysis based on the global burden of disease study 2019. BMC Public Health 2024; 24:295. [PMID: 38273270 PMCID: PMC10809510 DOI: 10.1186/s12889-023-17560-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 12/21/2023] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND The possibility of adverse effects of medical treatment (AEMT) is increasing worldwide, but little is known about AEMT in China. This study analyzed the health burden of AEMT in China in recent years through the Global Burden of Disease Study (GBD) 2019 and compared it with the worldwide average level and those in different sociodemographic index (SDI) regions. METHODS We calculated the age-standardized rate (ASR) of deaths, disability-adjusted life years (DALYs), years of life lost (YLLs), years lived with disability (YLDs), incidence and prevalence attributed to AEMT in China, worldwide and countries with different sociodemographic indices during 1990-2019 using the latest data and methods from the GBD 2019. RESULTS From 1990 to 2019, the global age-standardized death rate (ASDR), DALYs, and YLLs for AEMT showed a significant downward trend and were negatively associated with the SDI. By 2040, the ASDR is expected to reach approximately 1.58 (95% UI: 1.33-1.80). From 1990 to 2019, there was no significant change in the global incidence of AEMT. The occurrence of AEMT was related to sex, and the incidence of AEMT was greater among females. In addition, the incidence of AEMT-related injuries and burdens, such as ASR of DALYs, ASR of YLLs and ASR of YLDs, was greater among women than among men. Very old and very young people were more likely to be exposed to AEMT. CONCLUSIONS From 1990 to 2019, progress was made worldwide in reducing the harm caused by AEMT. However, the incidence and prevalence of AEMT did not change significantly overall during this period. Therefore, the health sector should pay more attention to AEMT and take effective measures to reduce AEMT.
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Affiliation(s)
- Xin Kong
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
- School of pharmacy, Dalian Medical University, Dalian, 116044, China
| | - Xufeng Tao
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
| | - Lu Li
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
| | - Xinya Zhao
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
- School of pharmacy, Dalian Medical University, Dalian, 116044, China
| | - Jiaqi Ren
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
- School of pharmacy, Dalian Medical University, Dalian, 116044, China
| | - Shilei Yang
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
| | - Xuyang Chen
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
| | - Hong Xiang
- Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China
| | - Guoyu Wu
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China.
| | - Yunming Li
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China.
| | - Deshi Dong
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China.
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Al-Worafi YM, Goh KW, Hermansyah A, Tan CS, Ming LC. The Use of ChatGPT for Education Modules on Integrated Pharmacotherapy of Infectious Disease: Educators' Perspectives. JMIR MEDICAL EDUCATION 2024; 10:e47339. [PMID: 38214967 PMCID: PMC10818233 DOI: 10.2196/47339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/21/2023] [Accepted: 07/25/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND Artificial Intelligence (AI) plays an important role in many fields, including medical education, practice, and research. Many medical educators started using ChatGPT at the end of 2022 for many purposes. OBJECTIVE The aim of this study was to explore the potential uses, benefits, and risks of using ChatGPT in education modules on integrated pharmacotherapy of infectious disease. METHODS A content analysis was conducted to investigate the applications of ChatGPT in education modules on integrated pharmacotherapy of infectious disease. Questions pertaining to curriculum development, syllabus design, lecture note preparation, and examination construction were posed during data collection. Three experienced professors rated the appropriateness and precision of the answers provided by ChatGPT. The consensus rating was considered. The professors also discussed the prospective applications, benefits, and risks of ChatGPT in this educational setting. RESULTS ChatGPT demonstrated the ability to contribute to various aspects of curriculum design, with ratings ranging from 50% to 92% for appropriateness and accuracy. However, there were limitations and risks associated with its use, including incomplete syllabi, the absence of essential learning objectives, and the inability to design valid questionnaires and qualitative studies. It was suggested that educators use ChatGPT as a resource rather than relying primarily on its output. There are recommendations for effectively incorporating ChatGPT into the curriculum of the education modules on integrated pharmacotherapy of infectious disease. CONCLUSIONS Medical and health sciences educators can use ChatGPT as a guide in many aspects related to the development of the curriculum of the education modules on integrated pharmacotherapy of infectious disease, syllabus design, lecture notes preparation, and examination preparation with caution.
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Affiliation(s)
- Yaser Mohammed Al-Worafi
- College of Medical Sciences, Azal University for Human Development, Sana'a, Yemen
- College of Pharmacy, University of Science and Technology of Fujairah, Fujairah, United Arab Emirates
| | - Khang Wen Goh
- Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
| | - Andi Hermansyah
- Department of Pharmacy Practice, Faculty of Pharmacy, Universitas Airlangga, Surabaya, Indonesia
| | - Ching Siang Tan
- School of Pharmacy, KPJ Healthcare University, Nilai, Malaysia
| | - Long Chiau Ming
- School of Medical and Life Sciences, Sunway University, Selangor, Malaysia
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Shankarnarayan SA, Charlebois DA. Machine learning to identify clinically relevant Candida yeast species. Med Mycol 2024; 62:myad134. [PMID: 38130236 DOI: 10.1093/mmy/myad134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/06/2023] [Accepted: 12/19/2023] [Indexed: 12/23/2023] Open
Abstract
Fungal infections, especially due to Candida species, are on the rise. Multi-drug resistant organisms such as Candida auris are difficult and time consuming to identify accurately. Machine learning is increasingly being used in health care, especially in medical imaging. In this study, we evaluated the effectiveness of six convolutional neural networks (CNNs) to identify four clinically important Candida species. Wet-mounted images were captured using bright field live-cell microscopy followed by separating single-cells, budding-cells, and cell-group images which were then subjected to different machine learning algorithms (custom CNN, VGG16, ResNet50, InceptionV3, EfficientNetB0, and EfficientNetB7) to learn and predict Candida species. Among the six algorithms tested, the InceptionV3 model performed best in predicting Candida species from microscopy images. All models performed poorly on raw images obtained directly from the microscope. The performance of all models increased when trained on single and budding cell images. The InceptionV3 model identified budding cells of C. albicans, C. auris, C. glabrata (Nakaseomyces glabrata), and C. haemulonii in 97.0%, 74.0%, 68.0%, and 66.0% cases, respectively. For single cells of C. albicans, C. auris, C. glabrata, and C. haemulonii InceptionV3 identified 97.0%, 73.0%, 69.0%, and 73.0% cases, respectively. The sensitivity and specificity of InceptionV3 were 77.1% and 92.4%, respectively. Overall, this study provides proof of the concept that microscopy images from wet-mounted slides can be used to identify Candida yeast species using machine learning quickly and accurately.
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Affiliation(s)
| | - Daniel A Charlebois
- Department of Physics, University of Alberta, Edmonton, Alberta, T6G-2E1, Canada
- Department of Physics, Department of Biological Sciences, University of Alberta, Edmonton, Alberta, T6G-2E9, Canada
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Rahman MA, Victoros E, Ernest J, Davis R, Shanjana Y, Islam MR. Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin. CLINICAL PATHOLOGY (THOUSAND OAKS, VENTURA COUNTY, CALIF.) 2024; 17:2632010X241226887. [PMID: 38264676 PMCID: PMC10804900 DOI: 10.1177/2632010x241226887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/27/2023] [Indexed: 01/25/2024]
Abstract
The influence of artificial intelligence (AI) has drastically risen in recent years, especially in the field of medicine. Its influence has spread so greatly that it is determined to become a pillar in the future medical world. A comprehensive literature search related to AI in healthcare was performed in the PubMed database and retrieved the relevant information from suitable ones. AI excels in aspects such as rapid adaptation, high diagnostic accuracy, and data management that can help improve workforce productivity. With this potential in sight, the FDA has continuously approved more machine learning (ML) software to be used by medical workers and scientists. However, there are few controversies such as increased chances of data breaches, concern for clinical implementation, and potential healthcare dilemmas. In this article, the positive and negative aspects of AI implementation in healthcare are discussed, as well as recommended some potential solutions to the potential issues at hand.
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Affiliation(s)
| | | | - Julianne Ernest
- Nesbitt School of Pharmacy Wilkes University, Wilkes-Barre, PA, USA
| | - Rob Davis
- Nesbitt School of Pharmacy Wilkes University, Wilkes-Barre, PA, USA
| | - Yeasna Shanjana
- Department of Environmental Sciences, North South University, Bashundhara, Dhaka, Bangladesh
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Badge A, Chandankhede M, Gajbe U, Bankar NJ, Bandre GR. Employment of Small-Group Discussions to Ensure the Effective Delivery of Medical Education. Cureus 2024; 16:e52655. [PMID: 38380198 PMCID: PMC10877665 DOI: 10.7759/cureus.52655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/21/2024] [Indexed: 02/22/2024] Open
Abstract
The changing landscape of medical education has made small-group discussions crucial components. These sessions, including problem-based learning (PBL), case-based learning (CBL), and team-based learning (TBL), revolutionize learning by fostering active participation, critical thinking, and practical skills application. They bridge theory with practice, preparing future healthcare professionals for the dynamic challenges of modern healthcare. Despite their transformative potential, there are challenges in faculty preparation, resource allocation, and effective evaluation. The best practices include aligning discussions with curriculum goals, skilled facilitation, promoting active participation, and robust assessment strategies. Looking ahead, adapting to emerging health trends, ongoing research, and evolving healthcare demands will ensure the continued relevance and effectiveness of small-group discussions, shaping competent and adaptable healthcare providers equipped for the ever-evolving healthcare landscape.
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Affiliation(s)
- Ankit Badge
- Microbiology, Datta Meghe Medical College, Datta Meghe Institute of Higher Education and Research, Nagpur, IND
| | - Manju Chandankhede
- Biochemistry, Datta Meghe Medical College, Datta Meghe Institute of Higher Education and Research, Nagpur, IND
| | - Ujwal Gajbe
- Anatomy, Datta Meghe Medical College, Datta Meghe Institute of Higher Education and Research, Nagpur, IND
| | - Nandkishor J Bankar
- Microbiology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Gulshan R Bandre
- Microbiology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Daher OA, Dabbousi AA, Chamroukh R, Saab AY, Al Ayoubi AR, Salameh P. Artificial Intelligence: Knowledge and Attitude Among Lebanese Medical Students. Cureus 2024; 16:e51466. [PMID: 38298326 PMCID: PMC10829838 DOI: 10.7759/cureus.51466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/01/2024] [Indexed: 02/02/2024] Open
Abstract
Background Artificial intelligence (AI) has taken on a variety of functions in the medical field, and research has proven that it can address complicated issues in various applications. It is unknown whether Lebanese medical students and residents have a detailed understanding of this concept, and little is known about their attitudes toward AI. Aim This study fills a critical gap by revealing the knowledge and attitude of Lebanese medical students toward AI. Methods A multi-centric survey targeting 365 medical students from seven medical schools across Lebanon was conducted to assess their knowledge of and attitudes toward AI in medicine. The survey consists of five sections: the first part includes socio-demographic variables, while the second comprises the 'Medical Artificial Intelligence Readiness Scale' for medical students. The third part focuses on attitudes toward AI in medicine, the fourth assesses understanding of deep learning, and the fifth targets considerations of radiology as a specialization. Results There is a notable awareness of AI among students who are eager to learn about it. Despite this interest, there exists a gap in knowledge regarding deep learning, albeit alongside a positive attitude towards it. Students who are more open to embracing AI technology tend to have a better understanding of AI concepts (p=0.001). Additionally, a higher percentage of students from Mount Lebanon (71.6%) showed an inclination towards using AI compared to Beirut (63.2%) (p=0.03). Noteworthy are the Lebanese University and Saint Joseph University, where the highest proportions of students are willing to integrate AI into the medical field (79.4% and 76.7%, respectively; p=0.001). Conclusion It was concluded that most Lebanese medical students might not necessarily comprehend the core technological ideas of AI and deep learning. This lack of understanding was evident from the substantial amount of misinformation among the students. Consequently, there appears to be a significant demand for the inclusion of AI technologies in Lebanese medical school courses.
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Affiliation(s)
- Omar A Daher
- Faculty of Medicine, Beirut Arab University, Beirut, LBN
| | | | | | | | - Amir Rabih Al Ayoubi
- Department of General Medicine, Faculty of Medical Sciences, Lebanese University, Beirut, LBN
| | - Pascale Salameh
- Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia, CYP
- Department of Public Health, Institut National de Santé Publique, d'Épidémiologie Clinique et de Toxicologie (INSPECT-LB), Beirut, LBN
- Department of Pharmacy Practice, Lebanese University, Beirut, LBN
- School of Medicine, Lebanese American University, Beirut, LBN
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Malik S, Zaheer S. ChatGPT as an aid for pathological diagnosis of cancer. Pathol Res Pract 2024; 253:154989. [PMID: 38056135 DOI: 10.1016/j.prp.2023.154989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/08/2023]
Abstract
Diagnostic workup of cancer patients is highly reliant on the science of pathology using cytopathology, histopathology, and other ancillary techniques like immunohistochemistry and molecular cytogenetics. Data processing and learning by means of artificial intelligence (AI) has become a spearhead for the advancement of medicine, with pathology and laboratory medicine being no exceptions. ChatGPT, an artificial intelligence (AI)-based chatbot, that was recently launched by OpenAI, is currently a talk of the town, and its role in cancer diagnosis is also being explored meticulously. Pathology workflow by integration of digital slides, implementation of advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enables effective integration, assimilation, and utilization of knowledge that is beyond human limits and boundaries. Despite of it's numerous advantages in the pathological diagnosis of cancer, it comes with several challenges like integration of digital slides with input language parameters, problems of bias, and legal issues which have to be addressed and worked up soon so that we as a pathologists diagnosing malignancies are on the same band wagon and don't miss the train.
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Affiliation(s)
- Shaivy Malik
- Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
| | - Sufian Zaheer
- Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.
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Zuhair V, Babar A, Ali R, Oduoye MO, Noor Z, Chris K, Okon II, Rehman LU. Exploring the Impact of Artificial Intelligence on Global Health and Enhancing Healthcare in Developing Nations. J Prim Care Community Health 2024; 15:21501319241245847. [PMID: 38605668 PMCID: PMC11010755 DOI: 10.1177/21501319241245847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI), which combines computer science with extensive datasets, seeks to mimic human-like intelligence. Subsets of AI are being applied in almost all fields of medicine and surgery. AIM This review focuses on the applications of AI in healthcare settings in developing countries, designed to underscore its significance by comprehensively outlining the advancements made thus far, the shortcomings encountered in AI applications, the present status of AI integration, persistent challenges, and innovative strategies to surmount them. METHODOLOGY Articles from PubMed, Google Scholar, and Cochrane were searched from 2000 to 2023 with keywords including AI and healthcare, focusing on multiple medical specialties. RESULTS The increasing role of AI in diagnosis, prognosis prediction, and patient management, as well as hospital management and community healthcare, has made the overall healthcare system more efficient, especially in the high patient load setups and resource-limited areas of developing countries where patient care is often compromised. However, challenges, including low adoption rates and the absence of standardized guidelines, high installation and maintenance costs of equipment, poor transportation and connectivvity issues hinder AI's full use in healthcare. CONCLUSION Despite these challenges, AI holds a promising future in healthcare. Adequate knowledge and expertise of healthcare professionals for the use of AI technology in healthcare is imperative in developing nations.
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Affiliation(s)
- Varisha Zuhair
- Jinnah Sindh Medical University, Karachi, Sindh, Pakistan
| | - Areesha Babar
- Jinnah Sindh Medical University, Karachi, Sindh, Pakistan
| | - Rabbiya Ali
- Jinnah Sindh Medical University, Karachi, Sindh, Pakistan
| | - Malik Olatunde Oduoye
- The Medical Research Circle, (MedReC), Gisenyi, Goma, Democratic Republic of the Congo
| | - Zainab Noor
- Institute of Dentistry CMH Lahore Medical College, Lahore, Punjab, Pakistan
| | - Kitumaini Chris
- The Medical Research Circle, (MedReC), Gisenyi, Goma, Democratic Republic of the Congo
- Université Libre des Pays des Grands-Lacs Goma, Noth-Kivu, Democratic Republic of the Congo
| | - Inibehe Ime Okon
- The Medical Research Circle, (MedReC), Gisenyi, Goma, Democratic Republic of the Congo
- NiMSA SCOPH, Uyo, Akwa-Ibom State, Nigeria
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Karunananthan S, Rahgozar A, Hakimjavadi R, Yan H, Dalsania KA, Bergman H, Ghose B, LaPlante J, McCutcheon T, McIsaac DI, Abbasgholizadeh Rahimi S, Sourial N, Thandi M, Wong ST, Liddy C. Use of Artificial Intelligence in the Identification and Management of Frailty: A Scoping Review Protocol. BMJ Open 2023; 13:e076918. [PMID: 38154888 PMCID: PMC10759108 DOI: 10.1136/bmjopen-2023-076918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 11/28/2023] [Indexed: 12/30/2023] Open
Abstract
INTRODUCTION Rapid population ageing and associated health issues such as frailty are a growing public health concern. While early identification and management of frailty may limit adverse health outcomes, the complex presentations of frailty pose challenges for clinicians. Artificial intelligence (AI) has emerged as a potential solution to support the early identification and management of frailty. In order to provide a comprehensive overview of current evidence regarding the development and use of AI technologies including machine learning and deep learning for the identification and management of frailty, this protocol outlines a scoping review aiming to identify and present available information in this area. Specifically, this protocol describes a review that will focus on the clinical tools and frameworks used to assess frailty, the outcomes that have been evaluated and the involvement of knowledge users in the development, implementation and evaluation of AI methods and tools for frailty care in clinical settings. METHODS AND ANALYSIS This scoping review protocol details a systematic search of eight major academic databases, including Medline, Embase, PsycInfo, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Ageline, Web of Science, Scopus and Institute of Electrical and Electronics Engineers (IEEE) Xplore using the framework developed by Arksey and O'Malley and enhanced by Levac et al and the Joanna Briggs Institute. The search strategy has been designed in consultation with a librarian. Two independent reviewers will screen titles and abstracts, followed by full texts, for eligibility and then chart the data using a piloted data charting form. Results will be collated and presented through a narrative summary, tables and figures. ETHICS AND DISSEMINATION Since this study is based on publicly available information, ethics approval is not required. Findings will be communicated with healthcare providers, caregivers, patients and research and health programme funders through peer-reviewed publications, presentations and an infographic. REGISTRATION DETAILS OSF Registries (https://doi.org/10.17605/OSF.IO/T54G8).
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Affiliation(s)
- Sathya Karunananthan
- Interdisciplinary School of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada
- Bruyere Research Institute, Ottawa, Ontario, Canada
| | - Arya Rahgozar
- The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- Department of Family Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Ramtin Hakimjavadi
- Bruyere Research Institute, Ottawa, Ontario, Canada
- Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Hui Yan
- Bruyere Research Institute, Ottawa, Ontario, Canada
- Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Kunal A Dalsania
- Interdisciplinary School of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada
- Bruyere Research Institute, Ottawa, Ontario, Canada
| | - Howard Bergman
- Department of Family Medicine, McGill University, Montreal, Quebec, Canada
| | - Bishwajit Ghose
- Faculty of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada
| | | | - Tess McCutcheon
- C.T. Lamont Primary Health Care Research Centre, Bruyère Research Institute, Ottawa, Ontario, Canada
| | - Daniel I McIsaac
- Anesthesiology and Pain Medicine, The Ottawa Hospital, Ottawa, Ontario, Canada
| | | | - Nadia Sourial
- Department of Health Management, Evaluation & Policy, Université de Montréal, Montreal, Québec, Canada
- Research Center of the Centre Hospitalier de l'Université de Montréal, Montreal, Quebec, Canada
| | - Manpreet Thandi
- Centre for Health Services and Policy Research, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sabrina T Wong
- Centre for Health Services and Policy Research, University of British Columbia, Vancouver, British Columbia, Canada
| | - Clare Liddy
- Department of Family Medicine, University of Ottawa, Ottawa, Ontario, Canada
- C.T. Lamont Primary Health Care Research Centre, Bruyère Research Institute, Ottawa, Ontario, Canada
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Qu Z, Nguyen QV, Lau CW, Johnston A, Kennedy PJ, Simoff S, Catchpoole D. Understanding cancer patient cohorts in virtual reality environment for better clinical decisions: a usability study. BMC Med Inform Decis Mak 2023; 23:295. [PMID: 38124044 PMCID: PMC10731816 DOI: 10.1186/s12911-023-02392-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Visualising patient genomic data in a cohort with embedding data analytics models can provide relevant and sensible patient comparisons to assist a clinician with treatment decisions. As immersive technology is actively used around the medical world, there is a rising demand for an efficient environment that can effectively display genomic data visualisations on immersive devices such as a Virtual Reality (VR) environment. The VR technology will allow clinicians, biologists, and computer scientists to explore a cohort of individual patients within the 3D environment. However, demonstrating the feasibility of the VR prototype needs domain users' feedback for future user-centred design and a better cognitive model of human-computer interactions. There is limited research work for collecting and integrating domain knowledge into the prototype design. OBJECTIVE A usability study for the VR prototype--Virtual Reality to Observe Oncology data Models (VROOM) was implemented. VROOM was designed based on a preliminary study among medical users. The goals of this usability study included establishing a baseline of user experience, validating user performance measures, and identifying potential design improvements that are to be addressed to improve efficiency, functionality, and end-user satisfaction. METHODS The study was conducted with a group of domain users (10 males, 10 females) with portable VR devices and camera equipment. These domain users included medical users such as clinicians and genetic scientists and computing domain users such as bioinformatics and data analysts. Users were asked to complete routine tasks based on a clinical scenario. Sessions were recorded and analysed to identify potential areas for improvement to the data visual analytics projects in the VR environment. The one-hour usability study included learning VR interaction gestures, running visual analytics tool, and collecting before and after feedback. The feedback was analysed with different methods to measure effectiveness. The statistical method Mann-Whitney U test was used to analyse various task performances among the different participant groups, and multiple data visualisations were created to find insights from questionnaire answers. RESULTS The usability study investigated the feasibility of using VR for genomic data analysis in domain users' daily work. From the feedback, 65% of the participants, especially clinicians (75% of them), indicated that the VR prototype is potentially helpful for domain users' daily work but needed more flexibility, such as allowing them to define their features for machine learning part, adding new patient data, and importing their datasets in a better way. We calculated the engaged time for each task and compared them among different user groups. Computing domain users spent 50% more time exploring the algorithms and datasets than medical domain users. Additionally, the medical domain users engaged in the data visual analytics parts (approximately 20%) longer than the computing domain users.
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Affiliation(s)
- Zhonglin Qu
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia.
| | - Quang Vinh Nguyen
- MARCS Institute and School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia
| | - Chng Wei Lau
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia
| | - Andrew Johnston
- School of Computer Science, University of Technology Sydney, Sydney, Australia
| | - Paul J Kennedy
- School of Computer Science, University of Technology Sydney, Sydney, Australia
| | - Simeon Simoff
- MARCS Institute and School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia
| | - Daniel Catchpoole
- School of Computer Science, University of Technology Sydney, Sydney, Australia.
- Biospecimen Research Services, Children's Cancer Research Unit, The Kids Research Institute, The Children's Hospital at Westmead, Sydney, Australia.
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Sagare N, Bankar NJ, Shahu S, Bandre GR. Transforming Healthcare: The Revolutionary Benefits of Cashless Healthcare Services. Cureus 2023; 15:e50971. [PMID: 38259368 PMCID: PMC10800297 DOI: 10.7759/cureus.50971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/22/2023] [Indexed: 01/24/2024] Open
Abstract
As cashless hospital services have grown in popularity, the healthcare sector has seen a tremendous transition. That means the payments are done in an electronic manner which is also known as e-pay. The article discusses the benefits of cashless healthcare services, which are transforming the healthcare sector by providing a streamlined, secure, and effective experience for patients and healthcare providers. Cashless healthcare facilities make use of cutting-edge technologies, including mobile applications, digital wallets, and secure internet platforms, to optimize the utilization of resources within the healthcare ecosystem and improve the overall patient experience. The incorporation of technology has led to revolutionary innovations that continue to redefine the way people access and experience health services. The advantages of cashless hospital services have transformed the healthcare sector by enhancing data security, facilitating transparent billing, and encouraging a patient-centered approach. Cashless services are a preferred method of payment for both consumers and organizations due to their convenience and accessibility. Patients can make payments using digital channels such as mobile payment applications, online payment gateways, or contactless payment cards, whether they are paying for medications, lab tests, or complicated surgeries. Cashless transactions drastically reduce administrative challenges for healthcare providers by eliminating the requirement for manual documentation, which facilitates quick electronic transactions and reduces processing times. As the billing and payment process becomes digitized and streamlined, doctors and medical personnel can focus more on treating and caring for patients. Additionally, much faster insurance claim processing and verification processes result in quicker pay-outs and minimize the financial burden on patients.
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Affiliation(s)
- Nikhil Sagare
- Hospital Administration, School of Allied Health Sciences, Datta Meghe Institute of Higher Education and Research, Nagpur, IND
| | - Nandkishor J Bankar
- Microbiology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Shivani Shahu
- Hospital Administration, School of Allied Health Sciences, Datta Meghe Institute of Higher Education and Research, Nagpur, IND
| | - Gulshan R Bandre
- Microbiology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Parvatikar PP, Patil S, Khaparkhuntikar K, Patil S, Singh PK, Sahana R, Kulkarni RV, Raghu AV. Artificial intelligence: Machine learning approach for screening large database and drug discovery. Antiviral Res 2023; 220:105740. [PMID: 37935248 DOI: 10.1016/j.antiviral.2023.105740] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/17/2023] [Accepted: 10/26/2023] [Indexed: 11/09/2023]
Abstract
Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer aided drug discovery. It has a prominent ability in screening drug targets from large molecular databases. Recently, a number of web servers have developed for quickly screening publicly accessible chemical databases. In a nutshell, deep learning algorithms and artificial neural networks have modernised the field. Several drug discovery processes have used machine learning and deep learning algorithms, including peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modelling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Although there are presently a wide variety of data-driven AI/ML tools available, the majority of these tools have, up to this point, been developed in the context of non-communicable diseases like cancer, and a number of obstacles have prevented the translation of these tools to the discovery of treatments against infectious diseases. In this review various aspects of AI and ML in virtual screening of large databases were discussed. Here, with an emphasis on antivirals as well as other disease, offers a perspective on the advantages, drawbacks, and hazards of AI/ML techniques in the search for innovative treatments.
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Affiliation(s)
- Prachi P Parvatikar
- Department of Biotechnology, Allied Health Science, BLDE (Deemed-to-be University), Vijayapur 586103, Karnataka, India.
| | - Sudha Patil
- Department of Pharmaceutics, BLDEA's SSM College of Pharmacy and Research Centre, Vijayapur 586 103, Karnataka, India
| | - Kedar Khaparkhuntikar
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, 500037, India
| | - Shruti Patil
- Department of Biotechnology, Allied Health Science, BLDE (Deemed-to-be University), Vijayapur 586103, Karnataka, India
| | - Pankaj K Singh
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, 500037, India
| | - R Sahana
- Department of Computer Science and Engineering, RV Institute of Technology and Management, 560076, Bengaluru, India
| | - Raghavendra V Kulkarni
- Department of Biotechnology, Allied Health Science, BLDE (Deemed-to-be University), Vijayapur 586103, Karnataka, India; Department of Pharmaceutics, BLDEA's SSM College of Pharmacy and Research Centre, Vijayapur 586 103, Karnataka, India
| | - Anjanapura V Raghu
- Department of Science and Technology, BLDE (Deemed-to-be University), Vijayapur 586103, Karnataka, India.
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Keles E, Bagci U. The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review. NPJ Digit Med 2023; 6:220. [PMID: 38012349 PMCID: PMC10682088 DOI: 10.1038/s41746-023-00941-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 10/05/2023] [Indexed: 11/29/2023] Open
Abstract
Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.
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Affiliation(s)
- Elif Keles
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA.
| | - Ulas Bagci
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA
- Northwestern University, Department of Biomedical Engineering, Chicago, IL, USA
- Department of Electrical and Computer Engineering, Chicago, IL, USA
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Akin O, Lema-Dopico A, Paudyal R, Konar AS, Chenevert TL, Malyarenko D, Hadjiiski L, Al-Ahmadie H, Goh AC, Bochner B, Rosenberg J, Schwartz LH, Shukla-Dave A. Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies. Cancers (Basel) 2023; 15:5468. [PMID: 38001728 PMCID: PMC10670574 DOI: 10.3390/cancers15225468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/27/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging-Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.
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Affiliation(s)
- Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Alfonso Lema-Dopico
- Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA
| | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA
| | | | | | - Dariya Malyarenko
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lubomir Hadjiiski
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hikmat Al-Ahmadie
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Alvin C. Goh
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Bernard Bochner
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jonathan Rosenberg
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Lawrence H. Schwartz
- Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA
| | - Amita Shukla-Dave
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA
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Nguyen TV, Diakiw SM, VerMilyea MD, Dinsmore AW, Perugini M, Perugini D, Hall JMM. Efficient automated error detection in medical data using deep-learning and label-clustering. Sci Rep 2023; 13:19587. [PMID: 37949906 PMCID: PMC10638377 DOI: 10.1038/s41598-023-45946-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 10/26/2023] [Indexed: 11/12/2023] Open
Abstract
Medical datasets inherently contain errors from subjective or inaccurate test results, or from confounding biological complexities. It is difficult for medical experts to detect these elusive errors manually, due to lack of contextual information, limiting data privacy regulations, and the sheer scale of data to be reviewed. Current methods for training robust artificial intelligence (AI) models on data containing mislabeled examples generally fall into one of several categories-attempting to improve the robustness of the model architecture, the regularization techniques used, the loss function used during training, or selecting a subset of data that contains cleaner labels. This last category requires the ability to efficiently detect errors either prior to or during training, either relabeling them or removing them completely. More recent progress in error detection has focused on using multi-network learning to minimize deleterious effects of errors on training, however, using many neural networks to reach a consensus on which data should be removed can be computationally intensive and inefficient. In this work, a deep-learning based algorithm was used in conjunction with a label-clustering approach to automate error detection. For dataset with synthetic label flips added, these errors were identified with an accuracy of up to 85%, while requiring up to 93% less computing resources to complete compared to a previous model consensus approach developed previously. The resulting trained AI models exhibited greater training stability and up to a 45% improvement in accuracy, from 69 to over 99% compared to the consensus approach, at least 10% improvement on using noise-robust loss functions in a binary classification problem, and a 51% improvement for multi-class classification. These results indicate that practical, automated a priori detection of errors in medical data is possible, without human oversight.
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Affiliation(s)
- T V Nguyen
- Presagen, Adelaide, SA, 5000, Australia.
- School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, 2522, Australia.
| | | | - M D VerMilyea
- Ovation Fertility, Austin, TX, 78731, USA
- Texas Fertility Center, Austin, TX, 78731, USA
| | - A W Dinsmore
- California Fertility Partners, Los Angeles, CA, 90025, USA
| | - M Perugini
- Presagen, Adelaide, SA, 5000, Australia
- Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5000, Australia
| | | | - J M M Hall
- Presagen, Adelaide, SA, 5000, Australia
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, SA, 5005, Australia
- School of Physical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia
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Palavicini G. Intelligent Health: Progress and Benefit of Artificial Intelligence in Sensing-Based Monitoring and Disease Diagnosis. SENSORS (BASEL, SWITZERLAND) 2023; 23:9053. [PMID: 38005442 PMCID: PMC10675666 DOI: 10.3390/s23229053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 10/20/2023] [Accepted: 10/26/2023] [Indexed: 11/26/2023]
Abstract
Technology has progressed and allows people to go further in multiple fields related to social issues. Medicine cannot be the exception, especially nowadays, when the COVID-19 pandemic has accelerated the use of technology to continue living meaningfully, but mainly in giving consideration to people who remain confined at home with health issues. Our research question is: how can artificial intelligence (AI) translated into technological devices be used to identify health issues, improve people's health, or prevent severe patient damage? Our work hypothesis is that technology has improved so much during the last decades that Medicine cannot remain apart from this progress. It must integrate technology into treatments so proper communication between intelligent devices and human bodies could better prevent health issues and even correct those already manifested. Consequently, we will answer: what has been the progress of Medicine using intelligent sensor-based devices? Which of those devices are the most used in medical practices? Which is the most benefited population, and what do physicians currently use this technology for? Could sensor-based monitoring and disease diagnosis represent a difference in how the medical praxis takes place nowadays, favouring prevention as opposed to healing?
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Affiliation(s)
- Gabriela Palavicini
- Department of Media and Digital Culture, Instituto Tecnológico y de Estudios Superiores de Monterrey, Mexico City 01389, Mexico
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42
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Weerarathna IN, Kamble AR, Luharia A. Artificial Intelligence Applications for Biomedical Cancer Research: A Review. Cureus 2023; 15:e48307. [PMID: 38058345 PMCID: PMC10697339 DOI: 10.7759/cureus.48307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/05/2023] [Indexed: 12/08/2023] Open
Abstract
Artificial intelligence (AI) has rapidly evolved and demonstrated its potential in transforming biomedical cancer research, offering innovative solutions for cancer diagnosis, treatment, and overall patient care. Over the past two decades, AI has played a pivotal role in revolutionizing various facets of cancer clinical research. In this comprehensive review, we delve into the diverse applications of AI across the cancer care continuum, encompassing radiodiagnosis, radiotherapy, chemotherapy, immunotherapy, targeted therapy, surgery, and nanotechnology. AI has revolutionized cancer diagnosis, enabling early detection and precise characterization through advanced image analysis techniques. In radiodiagnosis, AI-driven algorithms enhance the accuracy of medical imaging, making it an invaluable tool for clinicians in the detection and assessment of cancer. AI has also revolutionized radiotherapy, facilitating precise tumor boundary delineation, optimizing treatment planning, and enabling real-time adjustments to improve therapeutic outcomes while minimizing collateral damage to healthy tissues. In chemotherapy, AI models have emerged as powerful tools for predicting patient responses to different treatment regimens, allowing for more personalized and effective strategies. In immunotherapy, AI analyzes genetic and imaging data to select ideal candidates for treatment and predict responses. Targeted therapy has seen great advancements with AI, aiding in the identification of specific molecular targets for tailored treatments. AI plays a vital role in surgery by offering real-time navigation and support, enhancing surgical precision. Moreover, the synergy between AI and nanotechnology promises the development of personalized nanomedicines, offering more efficient and targeted cancer treatments. While challenges related to data quality, interpretability, and ethical considerations persist, the future of AI in cancer research holds tremendous promise for improving patient outcomes through advanced and individualized care.
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Affiliation(s)
- Induni N Weerarathna
- Biomedical Sciences, School of Allied Health Sciences, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Aahash R Kamble
- Artificial Intelligence and Data Science, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Anurag Luharia
- Radiotherapy, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Jeyaraman M, Ratna HVK, Jeyaraman N, Venkatesan A, Ramasubramanian S, Yadav S. Leveraging Artificial Intelligence and Machine Learning in Regenerative Orthopedics: A Paradigm Shift in Patient Care. Cureus 2023; 15:e49756. [PMID: 38161806 PMCID: PMC10757680 DOI: 10.7759/cureus.49756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2023] [Indexed: 01/03/2024] Open
Abstract
The integration of artificial intelligence (AI) and machine learning (ML) into regenerative orthopedics heralds a paradigm shift in clinical methodologies and patient management. This review article scrutinizes AI's role in augmenting diagnostic accuracy, refining predictive models, and customizing patient care in orthopedic medicine. Focusing on innovations such as KeyGene and CellNet, we illustrate AI's adeptness in navigating complex genomic datasets, cellular differentiation, and scaffold biodegradation, which are critical components of tissue engineering. Despite its transformative potential, AI's clinical adoption remains in its infancy, contending with challenges in validation, ethical oversight, and model training for clinical relevance. This review posits AI as a vital complement to human intelligence (HI), advocating for an interdisciplinary approach that merges AI's computational prowess with medical expertise to fulfill precision medicine's promise. By analyzing historical and contemporary developments in AI, from the foundational theories of McCullough and Pitts to sophisticated neural networks, the paper emphasizes the need for a synergistic alliance between AI and HI. This collaboration is imperative for improving surgical outcomes, streamlining therapeutic modalities, and enhancing the quality of patient care. Our article calls for robust interdisciplinary strategies to overcome current obstacles and harness AI's full potential in revolutionizing patient outcomes, thereby significantly contributing to the advancement of regenerative orthopedics and the broader field of scientific research.
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Affiliation(s)
- Madhan Jeyaraman
- Orthopaedics, ACS Medical College and Hospital, Dr. MGR Educational and Research Institute, Chennai, IND
| | | | - Naveen Jeyaraman
- Orthopaedics, ACS Medical College and Hospital, Dr. MGR Educational and Research Institute, Chennai, IND
| | | | | | - Sankalp Yadav
- Medicine, Shri Madan Lal Khurana Chest Clinic, New Delhi, IND
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Yelne S, Chaudhary M, Dod K, Sayyad A, Sharma R. Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare. Cureus 2023; 15:e49252. [PMID: 38143615 PMCID: PMC10744168 DOI: 10.7759/cureus.49252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 11/22/2023] [Indexed: 12/26/2023] Open
Abstract
This comprehensive review delves into the impact and challenges of Artificial Intelligence (AI) in nursing science and healthcare. AI has already demonstrated its transformative potential in these fields, with applications spanning from personalized care and diagnostic accuracy to predictive analytics and telemedicine. However, the integration of AI has its complexities, including concerns related to data privacy, ethical considerations, and biases in algorithms and datasets. The future of healthcare appears promising, with AI poised to advance diagnostics, treatment, and healthcare practices. Nevertheless, it is crucial to remember that AI should complement, not replace, healthcare professionals, preserving the essential human element of care. To maximize AI's potential in healthcare, interdisciplinary collaboration, ethical guidelines, and the protection of patient rights are essential. This review concludes with a call to action, emphasizing the need for ongoing research and collective efforts to ensure that AI contributes to improved healthcare outcomes while upholding the highest standards of ethics and patient-centered care.
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Affiliation(s)
- Seema Yelne
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Minakshi Chaudhary
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Karishma Dod
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Akhtaribano Sayyad
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Ranjana Sharma
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Joo H, Mathis MR, Tam M, James C, Han P, Mangrulkar RS, Friedman CP, Vydiswaran VGV. Applying AI and Guidelines to Assist Medical Students in Recognizing Patients With Heart Failure: Protocol for a Randomized Trial. JMIR Res Protoc 2023; 12:e49842. [PMID: 37874618 PMCID: PMC10630872 DOI: 10.2196/49842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/16/2023] [Accepted: 09/20/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND The integration of artificial intelligence (AI) into clinical practice is transforming both clinical practice and medical education. AI-based systems aim to improve the efficacy of clinical tasks, enhancing diagnostic accuracy and tailoring treatment delivery. As it becomes increasingly prevalent in health care for high-quality patient care, it is critical for health care providers to use the systems responsibly to mitigate bias, ensure effective outcomes, and provide safe clinical practices. In this study, the clinical task is the identification of heart failure (HF) prior to surgery with the intention of enhancing clinical decision-making skills. HF is a common and severe disease, but detection remains challenging due to its subtle manifestation, often concurrent with other medical conditions, and the absence of a simple and effective diagnostic test. While advanced HF algorithms have been developed, the use of these AI-based systems to enhance clinical decision-making in medical education remains understudied. OBJECTIVE This research protocol is to demonstrate our study design, systematic procedures for selecting surgical cases from electronic health records, and interventions. The primary objective of this study is to measure the effectiveness of interventions aimed at improving HF recognition before surgery, the second objective is to evaluate the impact of inaccurate AI recommendations, and the third objective is to explore the relationship between the inclination to accept AI recommendations and their accuracy. METHODS Our study used a 3 × 2 factorial design (intervention type × order of prepost sets) for this randomized trial with medical students. The student participants are asked to complete a 30-minute e-learning module that includes key information about the intervention and a 5-question quiz, and a 60-minute review of 20 surgical cases to determine the presence of HF. To mitigate selection bias in the pre- and posttests, we adopted a feature-based systematic sampling procedure. From a pool of 703 expert-reviewed surgical cases, 20 were selected based on features such as case complexity, model performance, and positive and negative labels. This study comprises three interventions: (1) a direct AI-based recommendation with a predicted HF score, (2) an indirect AI-based recommendation gauged through the area under the curve metric, and (3) an HF guideline-based intervention. RESULTS As of July 2023, 62 of the enrolled medical students have fulfilled this study's participation, including the completion of a short quiz and the review of 20 surgical cases. The subject enrollment commenced in August 2022 and will end in December 2023, with the goal of recruiting 75 medical students in years 3 and 4 with clinical experience. CONCLUSIONS We demonstrated a study protocol for the randomized trial, measuring the effectiveness of interventions using AI and HF guidelines among medical students to enhance HF recognition in preoperative care with electronic health record data. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/49842.
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Affiliation(s)
- Hyeon Joo
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, United States
| | - Michael R Mathis
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States
| | - Marty Tam
- Department of Internal Medicine, Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Cornelius James
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, United States
- Department of Pediatrics, University of Michigan, Ann Arbor, MI, United States
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Peijin Han
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Rajesh S Mangrulkar
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, United States
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Charles P Friedman
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, United States
- School of Information, University of Michigan, Ann Arbor, MI, United States
| | - V G Vinod Vydiswaran
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, United States
- School of Information, University of Michigan, Ann Arbor, MI, United States
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Hesso I, Kayyali R, Dolton DR, Joo K, Zacharias L, Charalambous A, Lavdaniti M, Stalika E, Ajami T, Acampa W, Boban J, Nabhani-Gebara S. Cancer care at the time of the fourth industrial revolution: an insight to healthcare professionals' perspectives on cancer care and artificial intelligence. Radiat Oncol 2023; 18:167. [PMID: 37814325 PMCID: PMC10561443 DOI: 10.1186/s13014-023-02351-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 09/13/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND The integration of Artificial Intelligence (AI) technology in cancer care has gained unprecedented global attention over the past few decades. This has impacted the way that cancer care is practiced and delivered across settings. The purpose of this study was to explore the perspectives and experiences of healthcare professionals (HCPs) on cancer treatment and the need for AI. This study is a part of the INCISIVE European Union H2020 project's development of user requirements, which aims to fully explore the potential of AI-based cancer imaging technologies. METHODS A mixed-methods research design was employed. HCPs participating in cancer care in the UK, Greece, Italy, Spain, Cyprus, and Serbia were first surveyed anonymously online. Twenty-seven HCPs then participated in semi-structured interviews. Appropriate statistical method was adopted to report the survey results by using SPSS. The interviews were audio recorded, verbatim transcribed, and then thematically analysed supported by NVIVO. RESULTS The survey drew responses from 95 HCPs. The occurrence of diagnostic delay was reported by 56% (n = 28/50) for breast cancer, 64% (n = 27/42) for lung cancer, 76% (n = 34/45) for colorectal cancer and 42% (n = 16/38) for prostate cancer. A proportion of participants reported the occurrence of false positives in the accuracy of the current imaging techniques used: 64% (n = 32/50) reported this for breast cancer, 60% (n = 25/42) for lung cancer, 51% (n = 23/45) for colorectal cancer and 45% (n = 17/38) for prostate cancer. All participants agreed that the use of technology would enhance the care pathway for cancer patients. Despite the positive perspectives toward AI, certain limitations were also recorded. The majority (73%) of respondents (n = 69/95) reported they had never utilised technology in the care pathway which necessitates the need for education and training in the qualitative finding; compared to 27% (n = 26/95) who had and were still using it. Most, 89% of respondents (n = 85/95) said they would be opened to providing AI-based services in the future to improve medical imaging for cancer care. Interviews with HCPs revealed lack of widespread preparedness for AI in oncology, several barriers to introducing AI, and a need for education and training. Provision of AI training, increasing public awareness of AI, using evidence-based technology, and developing AI based interventions that will not replace HCPs were some of the recommendations. CONCLUSION HCPs reported favourable opinions of AI-based cancer imaging technologies and noted a number of care pathway concerns where AI can be useful. For the future design and execution of the INCISIVE project and other comparable AI-based projects, the characteristics and recommendations offered in the current research can serve as a reference.
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Affiliation(s)
- Iman Hesso
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Reem Kayyali
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Debbie-Rose Dolton
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Kwanyoung Joo
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Lithin Zacharias
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Andreas Charalambous
- Cyprus University of Technology, Limassol, Cyprus
- University of Turku, Turku, Finland
| | | | - Evangelia Stalika
- International Hellenic University, Thessaloniki, Greece
- Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Tarek Ajami
- Urology Department, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Wanda Acampa
- Department of Advanced Biomedical Science, University of Naples Federico II, Naples, Italy
| | - Jasmina Boban
- Department of Radiology, Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000, Novi Sad, Serbia
- Diagnostic Imaging Center, Oncology Institute of Vojvodine, Put Dr Goldmana 4, 21204, Sremska Kamenica, Serbia
| | - Shereen Nabhani-Gebara
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK.
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Stam WT, Ingwersen EW, Ali M, Spijkerman JT, Kazemier G, Bruns ERJ, Daams F. Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery. Surg Today 2023; 53:1209-1215. [PMID: 36840764 PMCID: PMC10520164 DOI: 10.1007/s00595-023-02662-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/07/2023] [Indexed: 02/26/2023]
Abstract
Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the development of machine learning algorithms, has the potential to completely reshape surgical patient management. In this paper, we reflect on multiple issues facing the implementation of machine learning, from the development to the actual implementation of machine learning models in daily clinical practice, providing suggestions on the use of machine learning models for predicting postoperative complications after major abdominal surgery.
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Affiliation(s)
- Wessel T Stam
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
- AGEM Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
| | - Erik W Ingwersen
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
- AGEM Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
| | - Mahsoem Ali
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
| | - Jorik T Spijkerman
- Independent Consultant in Computational Intelligence, Amsterdam, The Netherlands
| | - Geert Kazemier
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
| | - Emma R J Bruns
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
| | - Freek Daams
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands.
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Ahuja AS, Rahimy E, Sridhar J. Tracking Online Interest in Artificial Intelligence in Ophthalmology Using Google Trends. Semin Ophthalmol 2023; 38:644-647. [PMID: 37095683 DOI: 10.1080/08820538.2023.2204919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 02/15/2023] [Accepted: 04/17/2023] [Indexed: 04/26/2023]
Abstract
PURPOSE To examine trends in internet search queries related to artificial intelligence (AI) in ophthalmology and determine the correlation between online interest in AI, capital investment in AI, and peer-reviewed indexed publications regarding AI and ophthalmology. METHODS Online search trends for "AI retina", "AI eye", and "AI healthcare" were obtained via Google Trends from 2016 to 2022 on a relative interest scale in 1-week intervals. Global venture financing of AI- and machine learning (ML)-focused companies in healthcare was tracked from 2010 to 2019 from the consulting company, Klynveld Peat Marwick Goerdeler (KPMG), and the technology market intelligence company, CB Insights. Citation count from pubmed.gov was determined using the search query "artificial intelligence retina" from 2012 to 2021. RESULTS An increasingly linear growth in online search trends for "AI retina", "AI eye", and "AI healthcare" keyword searches was observed between 2016 and 2022. Global venture financing of AI and ML companies in healthcare also increased exponentially over the same time frame. There was an exponential increase in citations with nearly a 10-fold increase as reported by PubMed from 2015 onwards for the "artificial intelligence retina" search query. There was a significant and positive correlation between online search trends and investment trends (correlation coefficients of 0.98-0.99 and p-values <0.05) and between online search trends and citation count trends (correlation coefficients of 0.98-0.99 and p-values <0.05). CONCLUSIONS These results demonstrate that the applications of AI and ML in ophthalmology are increasingly being investigated, financed, and formally researched, suggesting a prominent role for AI-derived tools in ophthalmology clinical practice in the near future.
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Affiliation(s)
- Abhimanyu S Ahuja
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, USA
| | - Ehsan Rahimy
- Department of Ophthalmology, Palo Alto Medical Foundation, Palo Alto, CA, USA
- Byers Eye Institute, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Jayanth Sridhar
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL, USA
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Cheng D, Liu D, Li X, Zhang Z, Mi Z, Tao W, Fu J, Fan H. Deep-Learning-Based Model for the Prediction of Cancer-Specific Survival in Patients with Spinal Chordoma. World Neurosurg 2023; 178:e835-e845. [PMID: 37586553 DOI: 10.1016/j.wneu.2023.08.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 08/05/2023] [Accepted: 08/07/2023] [Indexed: 08/18/2023]
Abstract
OBJECTIVE Spinal chordomas are locally aggressive and frequently recurrent tumors with a poor prognosis. Previous studies focused on a Cox regression model to predict the survival of patients with spinal chordoma. We aimed to develop a more effective model based on deep learning for prognosis prediction in spinal chordoma. METHODS Patients with spinal chordoma were gathered from the SEER database. Cox regression analysis was conducted to compare the influence of different clinical characteristics on cancer-specific survival. Two deep learning models, namely, DeepSurv and NMTLR, were developed, alongside 2 classic models, for the purpose of comparison. Performance of these models was evaluated by concordance index, Integrated Brier Score, receiver operating characteristic curves, Kaplan-Meier curves, and calibration curves. RESULTS A total of 258 spinal chordoma patients were included in the current study. The median follow-up time was 94 ± 52 months. Variables used for prognosis prediction consisted of age, primary site, tumor size, histologic grade, extension of surgery, tumor invasion, and metastasis. Comparing with conventional models, each deep learning model showed superior predictive performance, the C-index on the test cohort is 0.830 for DeepSurv and 0.804 for NMTLR, respectively. The DeepSurv model represented the best performance, with area under the curve of 0.843 in predicting 5-year survival and 0.880 in predicting 10-year survival. CONCLUSIONS We successfully constructed a deep learning model to predict the CSS of spinal chordoma patients and proved that it was more accurate and practical than conventional prediction model. Our deep learning model has the potential to guide clinicians in better care planning and decision-making.
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Affiliation(s)
- Debin Cheng
- Department of Orthopaedics, Xijing Hospital Affiliated to The Fourth Military Medical University, Xi'an, Shanxi, China
| | - Dong Liu
- Department of Orthopaedics, Xijing Hospital Affiliated to The Fourth Military Medical University, Xi'an, Shanxi, China
| | - Xian Li
- Department of Orthopaedics, Shenzhen University General Hospital, Shenzhen, China
| | - Zhao Zhang
- Department of Orthopaedics, Xijing Hospital Affiliated to The Fourth Military Medical University, Xi'an, Shanxi, China
| | - Zhenzhou Mi
- Department of Orthopaedics, Xijing Hospital Affiliated to The Fourth Military Medical University, Xi'an, Shanxi, China
| | - Weidong Tao
- Department of Orthopaedics, Xijing Hospital Affiliated to The Fourth Military Medical University, Xi'an, Shanxi, China
| | - Jun Fu
- Department of Orthopaedics, Xijing Hospital Affiliated to The Fourth Military Medical University, Xi'an, Shanxi, China
| | - Hongbin Fan
- Department of Orthopaedics, Xijing Hospital Affiliated to The Fourth Military Medical University, Xi'an, Shanxi, China.
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Kim K, Lim CY, Shin J, Chung MJ, Jung YG. Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in the maxillary sinus. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107708. [PMID: 37473588 DOI: 10.1016/j.cmpb.2023.107708] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND AND OBJECTIVE The cone-beam computed tomography (CBCT) provides three-dimensional volumetric imaging of a target with low radiation dose and cost compared with conventional computed tomography, and it is widely used in the detection of paranasal sinus disease. However, it lacks the sensitivity to detect soft tissue lesions owing to reconstruction constraints. Consequently, only physicians with expertise in CBCT reading can distinguish between inherent artifacts or noise and diseases, restricting the use of this imaging modality. The development of artificial intelligence (AI)-based computer-aided diagnosis methods for CBCT to overcome the shortage of experienced physicians has attracted substantial attention. However, advanced AI-based diagnosis addressing intrinsic noise in CBCT has not been devised, discouraging the practical use of AI solutions for CBCT. We introduce the development of AI-based computer-aided diagnosis for CBCT considering the intrinsic imaging noise and evaluate its efficacy and implications. METHODS We propose an AI-based computer-aided diagnosis method using CBCT with a denoising module. This module is implemented before diagnosis to reconstruct the internal ground-truth full-dose scan corresponding to an input CBCT image and thereby improve the diagnostic performance. The proposed method is model agnostic and compatible with various existing and future AI-based denoising or diagnosis models. RESULTS The external validation results for the unified diagnosis of sinus fungal ball, chronic rhinosinusitis, and normal cases show that the proposed method improves the micro-, macro-average area under the curve, and accuracy by 7.4, 5.6, and 9.6% (from 86.2, 87.0, and 73.4 to 93.6, 92.6, and 83.0%), respectively, compared with a baseline while improving human diagnosis accuracy by 11% (from 71.7 to 83.0%), demonstrating technical differentiation and clinical effectiveness. In addition, the physician's ability to evaluate the AI-derived diagnosis results may be enhanced compared with existing solutions. CONCLUSION This pioneering study on AI-based diagnosis using CBCT indicates that denoising can improve diagnostic performance and reader interpretability in images from the sinonasal area, thereby providing a new approach and direction to radiographic image reconstruction regarding the development of AI-based diagnostic solutions. Furthermore, we believe that the performance enhancement will expedite the adoption of automated diagnostic solutions using CBCT, especially in locations with a shortage of skilled clinicians and limited access to high-dose scanning.
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Affiliation(s)
- Kyungsu Kim
- Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea; Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Chae Yeon Lim
- Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Joongbo Shin
- Department of Otorhinolaryngology-Head and Neck Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Myung Jin Chung
- Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea; Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea; Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yong Gi Jung
- Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea; Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Department of Otorhinolaryngology-Head and Neck Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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