51
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Thameem M, Raj A, Berrouk A, Jaoude MA, AlHammadi AA. Artificial intelligence-based forecasting model for incinerator in sulfur recovery units to predict SO 2 emissions. Environ Res 2024; 249:118329. [PMID: 38325781 DOI: 10.1016/j.envres.2024.118329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 01/25/2024] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
Pollutant emissions from chemical plants are a major concern in the context of environmental safety. A reliable emission forecasting model can provide important information for optimizing the process and improving the environmental performance. In this work, forecasting models are developed for the prediction of SO2 emission from a Sulfur Recovery Unit (SRU). Since SRUs incorporate complex chemical reactions, first-principle models are not suitable to predict emission levels based on a given feed condition. Accordingly, artificial intelligence-based models such as standard machine learning (ML) algorithms, multi-layer perceptron (MLP), long short-term memory (LSTM), one-dimensional convolution (1D-CNN), and CNN-LSTM models were tested, and their performance was evaluated. The input features and hyperparameters of the models were optimized to achieve maximum performance. The performance was evaluated in terms of mean squared error (MSE) and mean absolute percentage Error (MAPE) for 1 h, 3 h and 5 h ahead of forecasting. The reported results show that the CNN-LSTM encoder-decoder model outperforms other tested models, with its superiority becoming more pronounced as the forecasting horizon increased from 1 h to 5 h. For the 5-h ahead forecasting, the proposed model showed a MAPE advantage of 17.23%, 4.41%, and 2.83%, respectively over the 1D-CNN, Deep LSTM, and single-layer LSTM models in the larger dataset.
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Affiliation(s)
- Muhammed Thameem
- Department of Chemical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Center for Catalysis and Separations, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Abhijeet Raj
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Abdallah Berrouk
- Department of Mechanical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Center for Catalysis and Separations, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Maguy A Jaoude
- Center for Catalysis and Separations, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Department of Chemistry, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Ali A AlHammadi
- Department of Chemical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Center for Catalysis and Separations, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates.
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52
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Li H, Gui X, Wang P, Yue Y, Li H, Fan X, Li X, Liu R. Research on rapid quality identification method of Panax notoginseng powder based on artificial intelligence sensory technology and multi-source information fusion technology. Food Chem 2024; 440:138210. [PMID: 38118320 DOI: 10.1016/j.foodchem.2023.138210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 11/13/2023] [Accepted: 12/11/2023] [Indexed: 12/22/2023]
Abstract
Panax notoginseng powder (PNP) has high medicinal value and is widely used in the medical and health food industries. However, the adulteration of PNP in the market has dramatically reduced its efficacy. Therefore, this study intends to use artificial intelligence sensory (AIS) and multi-source information fusion (MIF) technology to try to establish a quality evaluation system for different grades of PNP and adulterated Panax notoginseng powder (AD-PNP). The highest accuracy rate reached 100% in identifying PNP grade and adulteration. In the prediction of adulteration ratio and total saponin content, the optimal determination coefficients of the test set were 0.9965 and 0.9948, respectively, and the root mean square errors were 0.0109 and 0.0123, respectively. Therefore, the grade identification method of PNP and the evaluation system of AD-PNP based on AIS and MIF technology can rapidly and accurately evaluate the quality of PNP.
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Affiliation(s)
- Haiyang Li
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Xinjing Gui
- Henan University of Chinese Medicine, Zhengzhou, China; The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China; Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Panpan Wang
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China; Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Yousong Yue
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Han Li
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Xuehua Fan
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Xuelin Li
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China; Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China; Co-construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. China, Henan University of Chinese Medicine, Zhengzhou, China
| | - Ruixin Liu
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China; Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China; Co-construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. China, Henan University of Chinese Medicine, Zhengzhou, China.
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53
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Wang S, Qiu Y, Zhu F. An updated review of functional ingredients of Manuka honey and their value-added innovations. Food Chem 2024; 440:138060. [PMID: 38211407 DOI: 10.1016/j.foodchem.2023.138060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 11/19/2023] [Accepted: 11/20/2023] [Indexed: 01/13/2024]
Abstract
Manuka honey (MH) is a highly prized natural product from the nectar of Leptospermum scoparium flowers. Increased competition on the global market drives MH product innovations. This review updates comparative and non-comparative studies to highlight nutritional, therapeutic, bioengineering, and cosmetic values of MH. MH is a good source of phenolics and unique chemical compounds, such as methylglyoxal, dihydroxyacetone, leptosperin glyoxal, methylsyringate and leptosin. Based on the evidence from in vitro, in vivo and clinical studies, multifunctional bioactive compounds of MH have exhibited anti-oxidative, anti-inflammatory, immunomodulatory, anti-microbial, and anti-cancer activities. There are controversial topics related to MH, such as MH grading, safety/efficacy, implied benefits, and maximum levels of contaminants concerned. Artificial intelligence can optimize MH studies related to chemical analysis, toxicity prediction, multi-functional mechanism exploration and product innovation.
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Affiliation(s)
- Sunan Wang
- Canadian Food and Wine Institute, Niagara College, 135 Taylor Road, Niagara-on-the-Lake, Ontario L0S 1J0, Canada; School of Chemical Sciences, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
| | - Yi Qiu
- Division of Engineering Science, Faculty of Applied Science and Engineering, University of Toronto, 35 St. George Street, Toronto, Ontario M5S 1A4, Canada
| | - Fan Zhu
- School of Chemical Sciences, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
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54
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Shrestha N, Cho SK. Reply to Letter-to-the-Editor on ChatGPT for the Diagnosis and Treatment of Low Back Pain: A Comparative Analysis. Spine (Phila Pa 1976) 2024; 49:E152. [PMID: 38369782 DOI: 10.1097/brs.0000000000004961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 01/31/2024] [Indexed: 02/20/2024]
Affiliation(s)
- Nancy Shrestha
- Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY
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Qi C, Hu T, Zheng J, Li K, Zhou N, Zhou M, Chen Q. Artificial intelligence-based prediction model for the elemental occurrence form of tailings and mine wastes. Environ Res 2024; 249:118378. [PMID: 38311206 DOI: 10.1016/j.envres.2024.118378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/27/2024] [Accepted: 01/29/2024] [Indexed: 02/10/2024]
Abstract
With the advent of the second industrial revolution, mining and metallurgical processes generate large volumes of tailings and mine wastes (TMW), which worsens global environmental pollution. Studying the occurrence of metal and metalloid elements in TMW is an effective approach to evaluating pollution linked to TMW. However, traditional laboratory-based measurements are complicated and time-consuming; thus, an empirical method is urgently needed that can rapidly and accurately determine elemental occurrence forms. In this study, a model combining Bayesian optimization and random forest (RF) approaches was proposed to predict TMW occurrence forms. To build the RF model, a dataset of 2376 samples was obtained, with mineral composition, elemental properties, and total concentration composition used as inputs and the percentage of occurrence forms as the model output. The correlation coefficient (R), coefficient of determination, mean absolute error, root mean squared error, and root mean squared logarithmic error metrics were used for model evaluation. After Bayesian optimization, the optimal RF model achieved accurate predictive performance, with R values of 0.99 and 0.965 on the training and test sets, respectively. The feature significance was analyzed using feature importance and Shapley additive explanatory values, which revealed that the electronegativity and total concentration of the elements were the two features with the greatest influence on the model output. As the electronegativity of an element increases, its corresponding residual fraction content gradually decreases. This is because the solubility typically increases with the solvent's polarity and electronegativity. Overall, this study proposes an RF model based on the nature of TMW that can rapidly and accurately predict the percentage values of metal and metalloid element occurrence forms in TMW. This method can minimize testing time requirements and help to assess TMW pollution risks, as well as further promote safe TMW management and recycling.
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Affiliation(s)
- Chongchong Qi
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China
| | - Tao Hu
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China
| | - Jiashuai Zheng
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China
| | - Kechao Li
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China
| | - Nana Zhou
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China
| | - Min Zhou
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China
| | - Qiusong Chen
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China.
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56
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Hu J, Wan J, Xi J, Shi W, Qian H. AI-driven design of customized 3D-printed multi-layer capsules with controlled drug release profiles for personalized medicine. Int J Pharm 2024; 656:124114. [PMID: 38615804 DOI: 10.1016/j.ijpharm.2024.124114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/25/2024] [Accepted: 04/10/2024] [Indexed: 04/16/2024]
Abstract
Personalized medicine aims to effectively and efficiently provide customized drugs that cater to diverse populations, which is a significant yet challenging task. Recently, the integration of artificial intelligence (AI) and three-dimensional (3D) printing technology has transformed the medical field, and was expected to facilitate the efficient design and development of customized drugs through the synergy of their respective advantages. In this study, we present an innovative method that combines AI and 3D printing technology to design and fabricate customized capsules. Initially, we discretized and encoded the geometry of the capsule, simulated the dissolution process of the capsule with classical drug dissolution model, and verified it by experiments. Subsequently, we employed a genetic algorithm to explore the capsule geometric structure space and generate a complex multi-layer structure that satisfies the target drug release profiles, including stepwise release and zero-order release. Finally, Two model drugs, isoniazid and acetaminophen, were selected and fused deposition modeling (FDM) 3D printing technology was utilized to precisely print the AI-designed capsule. The reliability of the method was verified by comparing the in vitro release curve of the printed capsules with the target curve, and the f2 value was more than 50. Notably, accurate and autonomous design of the drug release curve was achieved mainly by changing the geometry of the capsule. This approach is expected to be applied to different drug needs and facilitate the development of customized oral dosage forms.
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Affiliation(s)
- Jingzhi Hu
- School of Science, China Pharmaceutical University, Nanjing, PR China
| | - Jiale Wan
- School of Science, China Pharmaceutical University, Nanjing, PR China
| | - Junting Xi
- School of Science, China Pharmaceutical University, Nanjing, PR China
| | - Wei Shi
- Center of Drug Discovery, State Key Laboratory of Natural Medicines and Jiangsu Key Laboratory of Drug Discovery for Metabolic Disease, China Pharmaceutical University, Nanjing, PR China
| | - Hai Qian
- Center of Drug Discovery, State Key Laboratory of Natural Medicines and Jiangsu Key Laboratory of Drug Discovery for Metabolic Disease, China Pharmaceutical University, Nanjing, PR China.
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57
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Yao XH, He ZC, Bian XW. [Accelerating the construction of digital and intelligentialized pathology and the prospects]. ZHONGHUA BING LI XUE ZA ZHI = CHINESE JOURNAL OF PATHOLOGY 2024; 53:424-429. [PMID: 38678321 DOI: 10.3760/cma.j.cn112151-20240221-00108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/29/2024]
Abstract
With the continuous development of informatization, digitalization and artificial intelligence technology, the working mode of the pathology department has gradually changed from the traditional manual check, paper circulation and physical carrier storage to the informatization process and digital storage. The traditional pathology discipline has ushered in unprecedented opportunities and challenges. Digital pathology department also emerge as the times require. Simultaneously, with the full integration of artificial intelligence technology in pathology department, the concept of "department of digital and intelligentialized pathology" was proposed. Based on information and digital technology, the digital intelligent pathology department integrates intelligent management system, optimizes the previous cumbersome management and workflow of the pathology department, develops advanced technologies such as intelligent material extraction, unmanned organization processing, artificial intelligence quality control, artificial intelligence diagnosis, and promotes the intelligent construction of the pathology department.
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Affiliation(s)
- X H Yao
- Institute of Pathology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
| | - Z C He
- Institute of Pathology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
| | - X W Bian
- Institute of Pathology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
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58
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Carter SM, Aquino YSJ, Carolan L, Frost E, Degeling C, Rogers WA, Scott IA, Bell KJ, Fabrianesi B, Magrabi F. How should artificial intelligence be used in Australian health care? Recommendations from a citizens' jury. Med J Aust 2024; 220:409-416. [PMID: 38629188 DOI: 10.5694/mja2.52283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 11/06/2023] [Indexed: 05/06/2024]
Abstract
OBJECTIVE To support a diverse sample of Australians to make recommendations about the use of artificial intelligence (AI) technology in health care. STUDY DESIGN Citizens' jury, deliberating the question: "Under which circumstances, if any, should artificial intelligence be used in Australian health systems to detect or diagnose disease?" SETTING, PARTICIPANTS Thirty Australian adults recruited by Sortition Foundation using random invitation and stratified selection to reflect population proportions by gender, age, ancestry, highest level of education, and residential location (state/territory; urban, regional, rural). The jury process took 18 days (16 March - 2 April 2023): fifteen days online and three days face-to-face in Sydney, where the jurors, both in small groups and together, were informed about and discussed the question, and developed recommendations with reasons. Jurors received extensive information: a printed handbook, online documents, and recorded presentations by four expert speakers. Jurors asked questions and received answers from the experts during the online period of the process, and during the first day of the face-to-face meeting. MAIN OUTCOME MEASURES Jury recommendations, with reasons. RESULTS The jurors recommended an overarching, independently governed charter and framework for health care AI. The other nine recommendation categories concerned balancing benefits and harms; fairness and bias; patients' rights and choices; clinical governance and training; technical governance and standards; data governance and use; open source software; AI evaluation and assessment; and education and communication. CONCLUSIONS The deliberative process supported a nationally representative sample of citizens to construct recommendations about how AI in health care should be developed, used, and governed. Recommendations derived using such methods could guide clinicians, policy makers, AI researchers and developers, and health service users to develop approaches that ensure trustworthy and responsible use of this technology.
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Affiliation(s)
- Stacy M Carter
- University of Wollongong, Wollongong, NSW
- Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, NSW
| | - Yves Saint James Aquino
- University of Wollongong, Wollongong, NSW
- Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, NSW
| | - Lucy Carolan
- University of Wollongong, Wollongong, NSW
- Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, NSW
| | - Emma Frost
- University of Wollongong, Wollongong, NSW
- Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, NSW
| | - Chris Degeling
- University of Wollongong, Wollongong, NSW
- Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, NSW
| | | | - Ian A Scott
- University of Queensland, Brisbane, QLD
- Princess Alexandra Hospital, Brisbane, QLD
| | | | - Belinda Fabrianesi
- University of Wollongong, Wollongong, NSW
- Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, NSW
| | - Farah Magrabi
- Australian Institute for Health Innovation, Macquarie University, Sydney, NSW
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Sullivan C, Pointon K. Artificial intelligence in health care: nothing about me without me. Med J Aust 2024; 220:407-408. [PMID: 38629208 DOI: 10.5694/mja2.52282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 04/03/2024] [Indexed: 05/06/2024]
Affiliation(s)
- Clair Sullivan
- Queensland Digital Health Centre, University of Queensland, Brisbane, QLD
| | - Keren Pointon
- Queensland Digital Health Centre, University of Queensland, Brisbane, QLD
- Health Consumers Queensland, Brisbane, QLD
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60
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Kewalramani D, Loftus TJ, Coleman JR, Kaafarani H, Narayan M. Using AI to bridge global surgical gaps: high tech, high impact. Lancet 2024; 403:1746-1747. [PMID: 38704161 DOI: 10.1016/s0140-6736(23)02753-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 12/06/2023] [Indexed: 05/06/2024]
Affiliation(s)
| | | | - Julia R Coleman
- The Ohio State University College of Medicine, Columbus, OH, USA
| | | | - Mayur Narayan
- Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA.
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Nandagopal M, Seerangan K, Govindaraju T, Abi NE, Balusamy B, Selvarajan S. A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems. Sci Rep 2024; 14:10280. [PMID: 38704423 DOI: 10.1038/s41598-024-59846-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 04/16/2024] [Indexed: 05/06/2024] Open
Abstract
In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients' medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier's error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL's effectiveness and efficiency in identifying diseases is evaluated and compared.
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Affiliation(s)
- Malarvizhi Nandagopal
- Department of CSE, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, 600062, India
| | - Koteeswaran Seerangan
- Department of CSE (AI&ML), S.A. Engineering College (Autonomous), Chennai, Tamil Nadu, 600077, India
| | - Tamilmani Govindaraju
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, 603203, India
| | - Neeba Eralil Abi
- Department of Information Technology, Rajagiri School of Engineering and Technology, Kochi, Kerala, 682039, India
| | - Balamurugan Balusamy
- Shiv Nadar (Institution of Eminence Deemed to be University), Greater Noida, Uttar Pradesh, 201314, India
| | - Shitharth Selvarajan
- Department of Computer Science, Kebri Dehar University, 250, Kebri Dehar, Ethiopia.
- School of Built Environment, Engineering and Computing, Leeds Beckett University, LS1 3HE, Leeds, UK.
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Gül Ş, Erdemir İ, Hanci V, Aydoğmuş E, Erkoç YS. How artificial intelligence can provide information about subdural hematoma: Assessment of readability, reliability, and quality of ChatGPT, BARD, and perplexity responses. Medicine (Baltimore) 2024; 103:e38009. [PMID: 38701313 DOI: 10.1097/md.0000000000038009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/05/2024] Open
Abstract
Subdural hematoma is defined as blood collection in the subdural space between the dura mater and arachnoid. Subdural hematoma is a condition that neurosurgeons frequently encounter and has acute, subacute and chronic forms. The incidence in adults is reported to be 1.72-20.60/100.000 people annually. Our study aimed to evaluate the quality, reliability and readability of the answers to questions asked to ChatGPT, Bard, and perplexity about "Subdural Hematoma." In this observational and cross-sectional study, we asked ChatGPT, Bard, and perplexity to provide the 100 most frequently asked questions about "Subdural Hematoma" separately. Responses from both chatbots were analyzed separately for readability, quality, reliability and adequacy. When the median readability scores of ChatGPT, Bard, and perplexity answers were compared with the sixth-grade reading level, a statistically significant difference was observed in all formulas (P < .001). All 3 chatbot responses were found to be difficult to read. Bard responses were more readable than ChatGPT's (P < .001) and perplexity's (P < .001) responses for all scores evaluated. Although there were differences between the results of the evaluated calculators, perplexity's answers were determined to be more readable than ChatGPT's answers (P < .05). Bard answers were determined to have the best GQS scores (P < .001). Perplexity responses had the best Journal of American Medical Association and modified DISCERN scores (P < .001). ChatGPT, Bard, and perplexity's current capabilities are inadequate in terms of quality and readability of "Subdural Hematoma" related text content. The readability standard for patient education materials as determined by the American Medical Association, National Institutes of Health, and the United States Department of Health and Human Services is at or below grade 6. The readability levels of the responses of artificial intelligence applications such as ChatGPT, Bard, and perplexity are significantly higher than the recommended 6th grade level.
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Affiliation(s)
- Şanser Gül
- Department of Neurosurgery, Ankara Ataturk Sanatory Education and Research Hospital, Ankara, Turkey
| | - İsmail Erdemir
- Department of Anesthesiology and Critical Care, Faculty of Medicine, Dokuz Eylül University, Izmir, Turkey
| | - Volkan Hanci
- Department of Anesthesiology and Reanimation, Ankara Sincan Education and Research Hospital, Ankara, Turkey
| | - Evren Aydoğmuş
- Department of Neurosurgery, Istanbul Kartal Dr Lütfi Kırdar City Hospital, Istanbul, Turkey
| | - Yavuz Selim Erkoç
- Department of Neurosurgery, Ankara Ataturk Sanatory Education and Research Hospital, Ankara, Turkey
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Abstract
Introduction The subject of this article is the discovery of dento-dental disharmony (DDD) at the end of treatment. Lack of diagnosis is the source of this type of disappointment. Material and Method The diagnosis of DDD is not easily accessible on clinical examination and the compensations it generates mask it, especially if it is associated with other dysmorphoses. The use of indices, the best-known of which is Bolton's, enables diagnosis with the setup, a pre-treatment model which also has many other prognostic interests. Results Once DDD has been considered, it can be resolved by adapting dental volumes, either by subtraction or addition. Conclusion Advances in computerized diagnosis with artificial intelligence are opening up new avenues for the systematic diagnosis of DDD.
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Mashoudy KD, Perez SM, Nouri K. From diagnosis to intervention: a review of telemedicine's role in skin cancer care. Arch Dermatol Res 2024; 316:139. [PMID: 38696032 PMCID: PMC11065900 DOI: 10.1007/s00403-024-02884-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/03/2024] [Accepted: 04/14/2024] [Indexed: 05/05/2024]
Abstract
Skin cancer treatment is a core aspect of dermatology that relies on accurate diagnosis and timely interventions. Teledermatology has emerged as a valuable asset across various stages of skin cancer care including triage, diagnosis, management, and surgical consultation. With the integration of traditional dermoscopy and store-and-forward technology, teledermatology facilitates the swift sharing of high-resolution images of suspicious skin lesions with consulting dermatologists all-over. Both live video conference and store-and-forward formats have played a pivotal role in bridging the care access gap between geographically isolated patients and dermatology providers. Notably, teledermatology demonstrates diagnostic accuracy rates that are often comparable to those achieved through traditional face-to-face consultations, underscoring its robust clinical utility. Technological advancements like artificial intelligence and reflectance confocal microscopy continue to enhance image quality and hold potential for increasing the diagnostic accuracy of virtual dermatologic care. While teledermatology serves as a valuable clinical tool for all patient populations including pediatric patients, it is not intended to fully replace in-person procedures like Mohs surgery and other necessary interventions. Nevertheless, its role in facilitating the evaluation of skin malignancies is gaining recognition within the dermatologic community and fostering high approval rates from patients due to its practicality and ability to provide timely access to specialized care.
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Affiliation(s)
- Kayla D Mashoudy
- University of Miami Miller School of Medicine, 1600 NW 10th Ave #1140, Miami, FL, 33136, USA.
| | - Sofia M Perez
- University of Miami Miller School of Medicine, 1600 NW 10th Ave #1140, Miami, FL, 33136, USA
| | - Keyvan Nouri
- Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, 1150 NW 14th Street, Miami, FL, 33136, USA
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Conejo-Rodríguez DF, Gonzalez-Guzman JJ, Ramirez-Gil JG, Wenzl P, Urban MO. Digital descriptors sharpen classical descriptors, for improving genebank accession management: A case study on Arachis spp. and Phaseolus spp. PLoS One 2024; 19:e0302158. [PMID: 38696404 PMCID: PMC11065210 DOI: 10.1371/journal.pone.0302158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 03/27/2024] [Indexed: 05/04/2024] Open
Abstract
High-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms. Our work proposes to improve the characterization processes of bean and peanut accessions in the CIAT genebank through the identification of phenomic descriptors comparable to classical descriptors including methodology integration into the genebank workflow. To cope with these goals morphometrics and colorimetry traits of 14 bean and 16 forage peanut accessions were determined and compared to the classical International Board for Plant Genetic Resources (IBPGR) descriptors. Descriptors discriminating most accessions were identified using a random forest algorithm. The most-valuable classification descriptors for peanuts were 100-seed weight and days to flowering, and for beans, days to flowering and primary seed color. The combination of phenomic and classical descriptors increased the accuracy of the classification of Phaseolus and Arachis accessions. Functional diversity indices are recommended to genebank curators to evaluate phenotypic variability to identify accessions with unique traits or identify accessions that represent the greatest phenotypic variation of the species (functional agrobiodiversity collections). The artificial intelligence algorithms are capable of characterizing accessions which reduces costs generated by additional phenotyping. Even though deep analysis of data requires new skills, associating genetic, morphological and ecogeographic diversity is giving us an opportunity to establish unique functional agrobiodiversity collections with new potential traits.
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Affiliation(s)
- Diego Felipe Conejo-Rodríguez
- Genetic Resources Program, International Center for Tropical Agriculture (CIAT), Palmira, Valle del Cauca, Colombia
- Bean Physiology and Breeding Program, International Center for Tropical Agriculture (CIAT), Palmira, Valle del Cauca, Colombia
- Facultad de Ciencias Agropecuarias, Universidad Nacional de Colombia Sede Palmira, Palmira, Valle del Cauca, Colombia
| | - Juan José Gonzalez-Guzman
- Genetic Resources Program, International Center for Tropical Agriculture (CIAT), Palmira, Valle del Cauca, Colombia
| | - Joaquín Guillermo Ramirez-Gil
- Departamento de Agronomía, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia Sede Bogotá, Bogotá, Colombia
| | - Peter Wenzl
- Genetic Resources Program, International Center for Tropical Agriculture (CIAT), Palmira, Valle del Cauca, Colombia
| | - Milan Oldřich Urban
- Bean Physiology and Breeding Program, International Center for Tropical Agriculture (CIAT), Palmira, Valle del Cauca, Colombia
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Chen G, Bai J, Ou Z, Lu Y, Wang H. PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head. Sci Data 2024; 11:436. [PMID: 38698003 PMCID: PMC11066050 DOI: 10.1038/s41597-024-03266-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 04/16/2024] [Indexed: 05/05/2024] Open
Abstract
During the process of labor, the intrapartum transperineal ultrasound examination serves as a valuable tool, allowing direct observation of the relative positional relationship between the pubic symphysis and fetal head (PSFH). Accurate assessment of fetal head descent and the prediction of the most suitable mode of delivery heavily rely on this relationship. However, achieving an objective and quantitative interpretation of the ultrasound images necessitates precise PSFH segmentation (PSFHS), a task that is both time-consuming and demanding. Integrating the potential of artificial intelligence (AI) in the field of medical ultrasound image segmentation, the development and evaluation of AI-based models rely significantly on access to comprehensive and meticulously annotated datasets. Unfortunately, publicly accessible datasets tailored for PSFHS are notably scarce. Bridging this critical gap, we introduce a PSFHS dataset comprising 1358 images, meticulously annotated at the pixel level. The annotation process adhered to standardized protocols and involved collaboration among medical experts. Remarkably, this dataset stands as the most expansive and comprehensive resource for PSFHS to date.
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Affiliation(s)
- Gaowen Chen
- Obstetrics and Gynecology Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jieyun Bai
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China.
- Auckland Bioengineering Institute, the University of Auckland, Auckland, New Zealand.
| | - Zhanhong Ou
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Yaosheng Lu
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China.
| | - Huijin Wang
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
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Esposito A, Desolda G, Lanzilotti R. The fine line between automation and augmentation in website usability evaluation. Sci Rep 2024; 14:10129. [PMID: 38698074 PMCID: PMC11066064 DOI: 10.1038/s41598-024-59616-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 04/11/2024] [Indexed: 05/05/2024] Open
Abstract
Artificial Intelligence (AI) systems are becoming widespread in all aspects of society, bringing benefits to the whole economy. There is a growing understanding of the potential benefits and risks of this type of technology. While the benefits are more efficient decision processes and industrial productivity, the risks may include a potential progressive disengagement of human beings in crucial aspects of decision-making. In this respect, a new perspective is emerging that aims at reconsidering the centrality of human beings while reaping the benefits of AI systems to augment rather than replace professional skills: Human-Centred AI (HCAI) is a novel framework that posits that high levels of human control do not contradict high levels of computer automation. In this paper, we investigate the two antipodes, automation vs augmentation, in the context of website usability evaluation. Specifically, we have analyzed whether the level of automation provided by a tool for semi-automatic usability evaluation can support evaluators in identifying usability problems. Three different visualizations, each one corresponding to a different level of automation, ranging from a full-automation approach to an augmentation approach, were compared in an experimental study. We found that a fully automated approach could help evaluators detect a significant number of medium and high-severity usability problems, which are the most critical in a software system; however, it also emerged that it was possible to detect more low-severity usability problems using one of the augmented approaches proposed in this paper.
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Affiliation(s)
- Andrea Esposito
- Department of Computer Science, University of Bari Aldo Moro, Via E. Orabona 4, 70125, Bari, Italy.
| | - Giuseppe Desolda
- Department of Computer Science, University of Bari Aldo Moro, Via E. Orabona 4, 70125, Bari, Italy
| | - Rosa Lanzilotti
- Department of Computer Science, University of Bari Aldo Moro, Via E. Orabona 4, 70125, Bari, Italy
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YAMAMURA E, HAYASHI R. AI, ageing and brain-work productivity: Technological change in professional Japanese chess. PLoS One 2024; 19:e0299889. [PMID: 38696493 PMCID: PMC11065245 DOI: 10.1371/journal.pone.0299889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 02/16/2024] [Indexed: 05/04/2024] Open
Abstract
Using Japanese professional chess (Shogi) players' records in the setting where various external factors are controlled in deterministic and finite games, this paper examines how and the extent to which the emergence of technological changes influences the ageing and innate ability of players' winning probability. We gathered games of professional Shogi players from 1968 to 2019, which we divided into three periods: 1968-1989, 1990-2012 (the diffusion of as information and communications technology (ICT)) and 2013-2019 (artificial intelligence (AI)). We found (1) diffusion of AI reduces the impact of innate ability in players performance. Consequently, the performance gap among same-age players has narrowed; (2) in all the periods, players' winning rates declined consistently from 20 years and as they get older; (3) AI accelerated the ageing decline of the probability of winning, which increased the performance gap among different aged players; (4) the effects of AI on the ageing decline and the probability of winning are observed for high innate skill players but not for low innate skill ones. The findings are specific to Shogi as a kind of board games although it is valuable to examine the extent to which the findings hold for other labor market.
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Affiliation(s)
- Eiji YAMAMURA
- Department of Economics, Seinan Gakuin University, Fukuoka, Japan
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Kuwabara M, Ikawa F, Nakazawa S, Koshino S, Ishii D, Kondo H, Hara T, Maeda Y, Sato R, Kaneko T, Maeyama S, Shimahara Y, Horie N. Artificial intelligence for volumetric measurement of cerebral white matter hyperintensities on thick-slice fluid-attenuated inversion recovery (FLAIR) magnetic resonance images from multiple centers. Sci Rep 2024; 14:10104. [PMID: 38698152 PMCID: PMC11065995 DOI: 10.1038/s41598-024-60789-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/26/2024] [Indexed: 05/05/2024] Open
Abstract
We aimed to develop a new artificial intelligence software that can automatically extract and measure the volume of white matter hyperintensities (WMHs) in head magnetic resonance imaging (MRI) using only thick-slice fluid-attenuated inversion recovery (FLAIR) sequences from multiple centers. We enrolled 1092 participants in Japan, comprising the thick-slice Private Dataset. Based on 207 randomly selected participants, neuroradiologists annotated WMHs using predefined guidelines. The annotated images of participants were divided into training (n = 138) and test (n = 69) datasets. The WMH segmentation model comprised a U-Net ensemble and was trained using the Private Dataset. Two other models were trained for validation using either both thin- and thick-slice MRI datasets or the thin-slice dataset alone. The voxel-wise Dice similarity coefficient (DSC) was used as the evaluation metric. The model trained using only thick-slice MRI showed a DSC of 0.820 for the test dataset, which is comparable to the accuracy of human readers. The model trained with the additional thin-slice dataset showed only a slightly improved DSC of 0.822. This automatic WMH segmentation model comprising a U-Net ensemble trained on a thick-slice FLAIR MRI dataset is a promising new method. Despite some limitations, this model may be applicable in clinical practice.
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Affiliation(s)
- Masashi Kuwabara
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Fusao Ikawa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8551, Japan.
- Department of Neurosurgery, Shimane Prefectural Central Hospital, 4-1-1 Himebara, Izumo, Shimane, 693-0068, Japan.
| | - Shinji Nakazawa
- LPIXEL Inc, 1-6-1 Otemachi, Chiyoda-Ku, Tokyo, 100-0004, Japan
| | - Saori Koshino
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Daizo Ishii
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Hiroshi Kondo
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Takeshi Hara
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Yuyo Maeda
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8551, Japan
| | - Ryo Sato
- LPIXEL Inc, 1-6-1 Otemachi, Chiyoda-Ku, Tokyo, 100-0004, Japan
| | - Taiki Kaneko
- LPIXEL Inc, 1-6-1 Otemachi, Chiyoda-Ku, Tokyo, 100-0004, Japan
| | - Shiyuki Maeyama
- LPIXEL Inc, 1-6-1 Otemachi, Chiyoda-Ku, Tokyo, 100-0004, Japan
| | - Yuki Shimahara
- LPIXEL Inc, 1-6-1 Otemachi, Chiyoda-Ku, Tokyo, 100-0004, Japan
| | - Nobutaka Horie
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8551, Japan
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Glielmo P, Fusco S, Gitto S, Zantonelli G, Albano D, Messina C, Sconfienza LM, Mauri G. Artificial intelligence in interventional radiology: state of the art. Eur Radiol Exp 2024; 8:62. [PMID: 38693468 PMCID: PMC11063019 DOI: 10.1186/s41747-024-00452-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 02/26/2024] [Indexed: 05/03/2024] Open
Abstract
Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored.Relevance statement Exploring AI's transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.Key points• AI adoption in IR is more complex compared to diagnostic radiology.• Current literature about AI in IR is in its early stages.• AI has the potential to revolutionise every aspect of IR.
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Affiliation(s)
- Pierluigi Glielmo
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy.
| | - Stefano Fusco
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
| | - Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Giulia Zantonelli
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
- Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Via della Commenda, 10, 20122, Milan, Italy
| | - Carmelo Messina
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Giovanni Mauri
- Divisione di Radiologia Interventistica, IEO, IRCCS Istituto Europeo di Oncologia, Milan, Italy
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Abuhasanein S, Edenbrandt L, Enqvist O, Jahnson S, Leonhardt H, Trägårdh E, Ulén J, Kjölhede H. A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria. Scand J Urol 2024; 59:90-97. [PMID: 38698545 DOI: 10.2340/sju.v59.39930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 04/17/2024] [Indexed: 05/05/2024]
Abstract
OBJECTIVE To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria. METHODS Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method. RESULTS The training cohort comprised a total of 530 patients. Following the optimisation process, we developed the last version of our AI model. Subsequently, we utilised the model in the validation cohort which included an additional 400 patients (including 239 patients with UBC). The AI model had a sensitivity of 0.83 (95% confidence intervals [CI], 0.76-0.89), specificity of 0.76 (95% CI 0.67-0.84), and a negative predictive value (NPV) of 0.97 (95% CI 0.95-0.98). The majority of tumours in the false negative group (n = 24) were solitary (67%) and smaller than 1 cm (50%), with the majority of patients having cTaG1-2 (71%). CONCLUSIONS We developed and tested an AI model for automatic image analysis of CTUs to detect UBC in patients with macroscopic hematuria. This model showed promising results with a high detection rate and excessive NPV. Further developments could lead to a decreased need for invasive investigations and prioritising patients with serious tumours.
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Affiliation(s)
- Suleiman Abuhasanein
- Department of Urology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden; Department of Surgery, Urology section, NU Hospital Group, Uddevalla, Region Västra Götaland, Sweden.
| | - Lars Edenbrandt
- Department of Clinical Physiology, Sahlgrenska University Hospital, Göteborg, Sweden; Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden
| | - Olof Enqvist
- Department of Electrical Engineering, Chalmers University of Technology, Göteborg, Sweden; Eigenvision AB, Malmö, Sweden
| | - Staffan Jahnson
- Department of Clinical and Experimental Medicine, Division of Urology, Linköping University, Linköping, Sweden
| | - Henrik Leonhardt
- Department of Radiology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden; Department of Radiology, Sahlgrenska University Hospital, Region Västra Götaland, Göteborg, Sweden
| | - Elin Trägårdh
- Department of Clinical Physiology and Nuclear Medicine, Lund University and Skåne University Hospital, Malmö, Sweden; Wallenberg Centre for Molecular Medicine, Lund University, Lund, Sweden
| | | | - Henrik Kjölhede
- Department of Urology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden; Department of Urology, Sahlgrenska University Hospital, Region Västra Götaland, Göteborg, Sweden
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Newlands R, Bruhn H, Díaz MR, Lip G, Anderson LA, Ramsay C. A stakeholder analysis to prepare for real-world evaluation of integrating artificial intelligent algorithms into breast screening (PREP-AIR study): a qualitative study using the WHO guide. BMC Health Serv Res 2024; 24:569. [PMID: 38698386 PMCID: PMC11067265 DOI: 10.1186/s12913-024-10926-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 03/28/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND The national breast screening programme in the United Kingdom is under pressure due to workforce shortages and having been paused during the COVID-19 pandemic. Artificial intelligence has the potential to transform how healthcare is delivered by improving care processes and patient outcomes. Research on the clinical and organisational benefits of artificial intelligence is still at an early stage, and numerous concerns have been raised around its implications, including patient safety, acceptance, and accountability for decisions. Reforming the breast screening programme to include artificial intelligence is a complex endeavour because numerous stakeholders influence it. Therefore, a stakeholder analysis was conducted to identify relevant stakeholders, explore their views on the proposed reform (i.e., integrating artificial intelligence algorithms into the Scottish National Breast Screening Service for breast cancer detection) and develop strategies for managing 'important' stakeholders. METHODS A qualitative study (i.e., focus groups and interviews, March-November 2021) was conducted using the stakeholder analysis guide provided by the World Health Organisation and involving three Scottish health boards: NHS Greater Glasgow & Clyde, NHS Grampian and NHS Lothian. The objectives included: (A) Identify possible stakeholders (B) Explore stakeholders' perspectives and describe their characteristics (C) Prioritise stakeholders in terms of importance and (D) Develop strategies to manage 'important' stakeholders. Seven stakeholder characteristics were assessed: their knowledge of the targeted reform, position, interest, alliances, resources, power and leadership. RESULTS Thirty-two participants took part from 14 (out of 17 identified) sub-groups of stakeholders. While they were generally supportive of using artificial intelligence in breast screening programmes, some concerns were raised. Stakeholder knowledge, influence and interests in the reform varied. Key advantages mentioned include service efficiency, quicker results and reduced work pressure. Disadvantages included overdiagnosis or misdiagnosis of cancer, inequalities in detection and the self-learning capacity of the algorithms. Five strategies (with considerations suggested by stakeholders) were developed to maintain and improve the support of 'important' stakeholders. CONCLUSIONS Health services worldwide face similar challenges of workforce issues to provide patient care. The findings of this study will help others to learn from Scottish experiences and provide guidance to conduct similar studies targeting healthcare reform. STUDY REGISTRATION researchregistry6579, date of registration: 16/02/2021.
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Affiliation(s)
- Rumana Newlands
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK.
| | - Hanne Bruhn
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK
| | | | - Gerald Lip
- North East Scotland Breast Screening Programme, NHS Grampian, Aberdeen, UK
| | - Lesley A Anderson
- Centre for Health Data Science, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Craig Ramsay
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK
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Khan AA, Yunus R, Sohail M, Rehman TA, Saeed S, Bu Y, Jackson CD, Sharkey A, Mahmood F, Matyal R. Artificial Intelligence for Anesthesiology Board-Style Examination Questions: Role of Large Language Models. J Cardiothorac Vasc Anesth 2024; 38:1251-1259. [PMID: 38423884 DOI: 10.1053/j.jvca.2024.01.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/24/2024] [Accepted: 01/29/2024] [Indexed: 03/02/2024]
Abstract
New artificial intelligence tools have been developed that have implications for medical usage. Large language models (LLMs), such as the widely used ChatGPT developed by OpenAI, have not been explored in the context of anesthesiology education. Understanding the reliability of various publicly available LLMs for medical specialties could offer insight into their understanding of the physiology, pharmacology, and practical applications of anesthesiology. An exploratory prospective review was conducted using 3 commercially available LLMs--OpenAI's ChatGPT GPT-3.5 version (GPT-3.5), OpenAI's ChatGPT GPT-4 (GPT-4), and Google's Bard--on questions from a widely used anesthesia board examination review book. Of the 884 eligible questions, the overall correct answer rates were 47.9% for GPT-3.5, 69.4% for GPT-4, and 45.2% for Bard. GPT-4 exhibited significantly higher performance than both GPT-3.5 and Bard (p = 0.001 and p < 0.001, respectively). None of the LLMs met the criteria required to secure American Board of Anesthesiology certification, according to the 70% passing score approximation. GPT-4 significantly outperformed GPT-3.5 and Bard in terms of overall performance, but lacked consistency in providing explanations that aligned with scientific and medical consensus. Although GPT-4 shows promise, current LLMs are not sufficiently advanced to answer anesthesiology board examination questions with passing success. Further iterations and domain-specific training may enhance their utility in medical education.
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Affiliation(s)
- Adnan A Khan
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Rayaan Yunus
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Mahad Sohail
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Taha A Rehman
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Shirin Saeed
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Yifan Bu
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Cullen D Jackson
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Aidan Sharkey
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Feroze Mahmood
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA
| | - Robina Matyal
- Department of Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA.
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74
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Zhang F, Zhan J, Wang Y, Cheng J, Wang M, Chen P, Ouyang J, Li J. Enhancing thalassemia gene carrier identification in non-anemic populations using artificial intelligence erythrocyte morphology analysis and machine learning. Eur J Haematol 2024; 112:692-700. [PMID: 38154920 DOI: 10.1111/ejh.14160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND Non-anemic thalassemia trait (TT) accounted for a high proportion of TT cases in South China. OBJECTIVE To use artificial intelligence (AI) analysis of erythrocyte morphology and machine learning (ML) to identify TT gene carriers in a non-anemic population. METHODS Digital morphological data from 76 TT gene carriers and 97 controls were collected. The AI technology-based Mindray MC-100i was used to quantitatively analyze the percentage of abnormal erythrocytes. Further, ML was used to construct a prediction model. RESULTS Non-anemic TT carriers accounted for over 60% of the TT cases. Random Forest was selected as the prediction model and named TT@Normal. The TT@Normal algorithm showed outstanding performance in the training, validation, and external validation sets and could efficiently identify TT carriers in the non-anemic population. The top three weights in the TT@Normal model were the target cells, microcytes, and teardrop cells. Elevated percentages of abnormal erythrocytes should raise a strong suspicion of being a TT gene carrier. TT@Normal could be promoted and used as a visualization and sharing tool. It is accessible through a URL link and can be used by medical staff online to predict the possibility of TT gene carriage in a non-anemic population. CONCLUSIONS The ML-based model TT@Normal could efficiently identify TT carriers in non-anemic people. Elevated percentages of target cells, microcytes, and teardrop cells should raise a strong suspicion of being a TT gene carrier.
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Affiliation(s)
- Fan Zhang
- Department of Laboratory Science, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jieyu Zhan
- Department of Pediatric, Baiyun District Maternal and Child Healthcare Centre, Guangzhou, China
| | - Yang Wang
- Department of Laboratory Science, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jing Cheng
- Department of Laboratory Science, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Meinan Wang
- IVD Domestic Clinical Application Department, Mindray Biomedical Electronics Co., Ltd, Shenzhen City, China
| | - Peisong Chen
- Department of Laboratory Science, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Juan Ouyang
- Department of Laboratory Science, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Junxun Li
- Department of Laboratory Science, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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75
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Fidon L, Aertsen M, Kofler F, Bink A, David AL, Deprest T, Emam D, Guffens F, Jakab A, Kasprian G, Kienast P, Melbourne A, Menze B, Mufti N, Pogledic I, Prayer D, Stuempflen M, Van Elslander E, Ourselin S, Deprest J, Vercauteren T. A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation. IEEE Trans Pattern Anal Mach Intell 2024; 46:3784-3795. [PMID: 38198270 DOI: 10.1109/tpami.2023.3346330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of four backbone AI models for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities.
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76
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Lechien JR, Carroll TL, Huston MN, Naunheim MR. ChatGPT-4 accuracy for patient education in laryngopharyngeal reflux. Eur Arch Otorhinolaryngol 2024; 281:2547-2552. [PMID: 38492008 DOI: 10.1007/s00405-024-08560-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 02/13/2024] [Indexed: 03/18/2024]
Abstract
INTRODUCTION Chatbot Generative Pre-trained Transformer (ChatGPT) is an artificial intelligence-powered language model chatbot able to help otolaryngologists in practice and research. The ability of ChatGPT in generating patient-centered information related to laryngopharyngeal reflux disease (LPRD) was evaluated. METHODS Twenty-five questions dedicated to definition, clinical presentation, diagnosis, and treatment of LPRD were developed from the Dubai definition and management of LPRD consensus and recent reviews. Questions about the four aforementioned categories were entered into ChatGPT-4. Four board-certified laryngologists evaluated the accuracy of ChatGPT-4 with a 5-point Likert scale. Interrater reliability was evaluated. RESULTS The mean scores (SD) of ChatGPT-4 answers for definition, clinical presentation, additional examination, and treatments were 4.13 (0.52), 4.50 (0.72), 3.75 (0.61), and 4.18 (0.47), respectively. Experts reported high interrater reliability for sub-scores (ICC = 0.973). The lowest performances of ChatGPT-4 were on answers about the most prevalent LPR signs, the most reliable objective tool for the diagnosis (hypopharyngeal-esophageal multichannel intraluminal impedance-pH monitoring (HEMII-pH)), and the criteria for the diagnosis of LPR using HEMII-pH. CONCLUSION ChatGPT-4 may provide adequate information on the definition of LPR, differences compared to GERD (gastroesophageal reflux disease), and clinical presentation. Information provided upon extra-laryngeal manifestations and HEMII-pH may need further optimization. Regarding the recent trends identifying increasing patient use of internet sources for self-education, the findings of the present study may help draw attention to ChatGPT-4's accuracy on the topic of LPR.
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Affiliation(s)
- Jerome R Lechien
- Research Committee, Young Otolaryngologists of the International Federation of Otorhinolaryngological Societies (IFOS), Paris, France.
- Division of Laryngology and Broncho-Esophagology, Department of Otolaryngology-Head Neck Surgery, EpiCURA Hospital, UMONS Research Institute for Health Sciences and Technology, University of Mons (UMons), Mons, Belgium.
- Department of Otorhinolaryngology and Head and Neck Surgery, Foch Hospital, School of Medicine, Phonetics and Phonology Laboratory (UMR 7018 CNRS, Université Sorbonne Nouvelle/Paris 3), Paris, France.
- Polyclinique Elsan de Poitiers, Poitiers, France.
| | - Thomas L Carroll
- Division of Otolaryngology-Head and Neck Surgery, Brigham and Women's Hospital, Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Molly N Huston
- Department of Otolaryngology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Matthew R Naunheim
- Research Committee, Young Otolaryngologists of the International Federation of Otorhinolaryngological Societies (IFOS), Paris, France
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
- Division of Laryngology, Massachusetts Eye and Ear, Boston, MA, USA
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Qu J, Xiao X, Wei X, Qian X. A causality-inspired generalized model for automated pancreatic cancer diagnosis. Med Image Anal 2024; 94:103154. [PMID: 38552527 DOI: 10.1016/j.media.2024.103154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 02/29/2024] [Accepted: 03/20/2024] [Indexed: 04/16/2024]
Abstract
Pancreatic cancer (PC) is a severely malignant cancer variant with high mortality. Since PC has no obvious symptoms, most PC patients are belatedly diagnosed at advanced disease stages. Recently, artificial intelligence (AI) approaches have demonstrated promising prospects for early diagnosis of pancreatic cancer. However, certain non-causal factors (such as intensity and texture appearance variations, also called confounders) tend to induce spurious correlation with PC diagnosis. This undermines the generalization performance and the clinical applicability of the AI-based PC diagnosis approaches. Therefore, we propose a causal intervention based automated method for pancreatic cancer diagnosis with contrast-enhanced computerized tomography (CT) images, where a confounding effects reduction scheme is developed for alleviating spurious correlations to achieve unbiased learning, thereby improving the generalization performance. Specifically, a continuous image generation strategy was developed to simulate wide variations of intensity differences caused by imaging heterogeneities, where Monte Carlo sampling is added to further enhance the continuity of simulated images. Then, to enhance the pancreatic texture variability, a texture diversification method was introduced in conjunction with gradient-based data augmentation. Finally, a causal intervention strategy was proposed to alleviate the adverse confounding effects by decoupling the causal and non-causal factors and combining them randomly. Extensive experiments showed remarkable diagnosis performance on a cross-validation dataset. Also, promising generalization performance with an average accuracy of 0.87 was attained on three independent test sets of a total of 782 subjects. Therefore, the proposed method shows high clinical feasibility and applicability for pancreatic cancer diagnosis.
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Affiliation(s)
- Jiaqi Qu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Xiang Xiao
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, PR China
| | - Xunbin Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China; Peking University Cancer Hospital & Institute, Beijing, 100142, PR China; Biomedical Engineering Department, Peking University, Beijing, 100081, PR China; Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, PR China; International Cancer Institute, Peking University, Beijing 100191, PR China.
| | - Xiaohua Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China.
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Hirpara MM, Amin L, Aloyan T, Shilleh N, Lewis P. Does the Internet Provide Quality Information on Metoidioplasty? Using the Modified Ensuring Quality Information for Patients Tool to Evaluate Artificial Intelligence-Generated and Online Information on Metoidioplasty. Ann Plast Surg 2024; 92:S361-S365. [PMID: 38689420 DOI: 10.1097/sap.0000000000003797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
BACKGROUND Public interest in gender affirmation surgery has surged over the last decade. This spike in interest, combined with extensive free online medical knowledge, has led many to the Internet for more information on this complicated procedure. This study aimed to evaluate the quality of online information on metoidioplasty. METHODS Google Trends in searches on "metoidioplasty" from 2004 to present were assessed. "metoidioplasty" was searched on three popular search engines (Google, Yahoo, and Bing), and the first 100 websites from each search were extracted for inclusion (Fig. 1). Exclusion criteria included duplicates, websites requiring fees, photo libraries, and irrelevant websites. Websites were assigned a score (out of 36) using the modified Ensuring Quality Information for Patients (EQIP) instrument, which grades patient materials based on content (18), identification (6), and structure (12). ChatGPT was also queried for metoidioplasty-related information and responses were analyzed using EQIP. RESULTS Google Trends analysis indicated relative search interest in "metoidioplasty" has more than quadrupled since 2013(Fig. 2). Of the 93 websites included, only 2 received an EQIP score greater than 27 (6%). Website scores ranged from 7 to 33, with a mean of 18.6 ± 4.8. Mean scores were highest for websites made by health departments (22.3) and lowest for those made by encyclopedias and academic institutions (16.0). Websites with the highest frequency were research articles, web portals, hospital websites, and private practice sites, which averaged scores of 18.2, 19.7, 19.0, and 17.8, respectively. Health department sites averaged the highest content points (11.25), and academic institutions averaged the lowest (5.5). The average content point across all websites was 7.9 of 18. ChatGPT scored a total score of 29: 17 content, 2 identification, and 10 structures. The artificial intelligence chatbot scored the second highest score among all included online resources. CONCLUSIONS Despite the continued use of search engines, the quality of online information on metoidioplasty remains exceptionally poor across most website developers. This study demonstrates the need to improve these resources, especially as interest in gender-affirming surgery continues to grow. ChatGPT and other artificial intelligence chatbots may be efficient and reliable alternatives for those seeking to understand complex medical information.
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Affiliation(s)
- Milan M Hirpara
- From the California University of Science and Medicine School of Medicine, Colton, CA
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Fournier A, Fallet C, Sadeghipour F, Perrottet N. Assessing the applicability and appropriateness of ChatGPT in answering clinical pharmacy questions. Ann Pharm Fr 2024; 82:507-513. [PMID: 37992892 DOI: 10.1016/j.pharma.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 11/16/2023] [Accepted: 11/16/2023] [Indexed: 11/24/2023]
Abstract
OBJECTIVES Clinical pharmacists rely on different scientific references to ensure appropriate, safe, and cost-effective drug use. Tools based on artificial intelligence (AI) such as ChatGPT (Generative Pre-trained Transformer) could offer valuable support. The objective of this study was to assess ChatGPT's capacity to correctly respond to clinical pharmacy questions asked by healthcare professionals in our university hospital. MATERIAL AND METHODS ChatGPT's capacity to respond correctly to the last 100 consecutive questions recorded in our clinical pharmacy database was assessed. Questions were copied from our FileMaker Pro database and pasted into ChatGPT March 14 version online platform. The generated answers were then copied verbatim into an Excel file. Two blinded clinical pharmacists reviewed all the questions and the answers given by the software. In case of disagreements, a third blinded pharmacist intervened to decide. RESULTS Documentation-related issues (n=36) and drug administration mode (n=30) were preponderantly recorded. Among 69 applicable questions, the rate of correct answers varied from 30 to 57.1% depending on questions type with a global rate of 44.9%. Regarding inappropriate answers (n=38), 20 were incorrect, 18 gave no answers and 8 were incomplete with 8 answers belonging to 2 different categories. No better answers than the pharmacists were observed. CONCLUSIONS ChatGPT demonstrated a mitigated performance in answering clinical pharmacy questions. It should not replace human expertise as a high rate of inappropriate answers was highlighted. Future studies should focus on the optimization of ChatGPT for specific clinical pharmacy questions and explore the potential benefits and limitations of integrating this technology into clinical practice.
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Affiliation(s)
- A Fournier
- Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - C Fallet
- Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - F Sadeghipour
- Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland; Center for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - N Perrottet
- Service of Pharmacy, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland.
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Elahmedi M, Sawhney R, Guadagno E, Botelho F, Poenaru D. The State of Artificial Intelligence in Pediatric Surgery: A Systematic Review. J Pediatr Surg 2024; 59:774-782. [PMID: 38418276 DOI: 10.1016/j.jpedsurg.2024.01.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 01/22/2024] [Indexed: 03/01/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has been recently shown to improve clinical workflows and outcomes - yet its potential in pediatric surgery remains largely unexplored. This systematic review details the use of AI in pediatric surgery. METHODS Nine medical databases were searched from inception until January 2023, identifying articles focused on AI in pediatric surgery. Two authors reviewed full texts of eligible articles. Studies were included if they were original investigations on the development, validation, or clinical application of AI models for pediatric health conditions primarily managed surgically. Studies were excluded if they were not peer-reviewed, were review articles, editorials, commentaries, or case reports, did not focus on pediatric surgical conditions, or did not employ at least one AI model. Extracted data included study characteristics, clinical specialty, AI method and algorithm type, AI model (algorithm) role and performance metrics, key results, interpretability, validation, and risk of bias using PROBAST and QUADAS-2. RESULTS Authors screened 8178 articles and included 112. Half of the studies (50%) reported predictive models (for adverse events [25%], surgical outcomes [16%] and survival [9%]), followed by diagnostic (29%) and decision support models (21%). Neural networks (44%) and ensemble learners (36%) were the most commonly used AI methods across application domains. The main pediatric surgical subspecialties represented across all models were general surgery (31%) and neurosurgery (25%). Forty-four percent of models were interpretable, and 6% were both interpretable and externally validated. Forty percent of models had a high risk of bias, and concerns over applicability were identified in 7%. CONCLUSIONS While AI has wide potential clinical applications in pediatric surgery, very few published AI algorithms were externally validated, interpretable, and unbiased. Future research needs to focus on developing AI models which are prospectively validated and ultimately integrated into clinical workflows. LEVEL OF EVIDENCE 2A.
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Affiliation(s)
- Mohamed Elahmedi
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
| | - Riya Sawhney
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
| | - Elena Guadagno
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
| | - Fabio Botelho
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
| | - Dan Poenaru
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada.
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Zhou K, Gattinger G. The Evolving Regulatory Paradigm of AI in MedTech: A Review of Perspectives and Where We Are Today. Ther Innov Regul Sci 2024; 58:456-464. [PMID: 38528278 PMCID: PMC11043174 DOI: 10.1007/s43441-024-00628-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/04/2024] [Indexed: 03/27/2024]
Abstract
Artificial intelligence (AI)-enabled technologies in the MedTech sector hold the promise to transform healthcare delivery by improving access, quality, and outcomes. As the regulatory contours of these technologies are being defined, there is a notable lack of literature on the key stakeholders such as the organizations and interest groups that have a significant input in shaping the regulatory framework. This article explores the perspectives and contributions of these stakeholders in shaping the regulatory paradigm of AI-enabled medical technologies. The formation of an AI regulatory framework requires the convergence of ethical, regulatory, technical, societal, and practical considerations. These multiple perspectives contribute to the various dimensions of an evolving regulatory paradigm. From the global governance guidelines set by the World Health Organization (WHO) to national regulations, the article sheds light not just on these multiple perspectives but also on their interconnectedness in shaping the regulatory landscape of AI.
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Affiliation(s)
- Karen Zhou
- Northeastern University, Toronto, ON, Canada.
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Kingsford PA, Ambrose JA. Artificial Intelligence and the Doctor-Patient Relationship. Am J Med 2024; 137:381-382. [PMID: 38281657 DOI: 10.1016/j.amjmed.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 01/30/2024]
Affiliation(s)
- Philip A Kingsford
- Department of Medicine, Division of Cardiovascular Medicine, University of California San Francisco, Fresno
| | - John A Ambrose
- Department of Medicine, Division of Cardiovascular Medicine, University of California San Francisco, Fresno.
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83
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Kuhn S, Knitza J. [Orthopedics and trauma surgery in the digital age]. Orthopadie (Heidelb) 2024; 53:327-335. [PMID: 38538858 DOI: 10.1007/s00132-024-04496-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/06/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND Digital transformation is shaping the future of orthopedics and trauma surgery. Telemedicine, digital health applications, electronic patient records and artificial intelligence play a central role in this. These technologies have the potential to improve medical care, enable individualized patient treatment plans and reduce the burden on the treatment process. However, there are currently challenges in the areas of infrastructure, regulation, reimbursement and data protection. REALISING THE TRANSFORMATION Effective transformation requires a deep understanding of both technology and clinical practice. Orthopedic and trauma surgeons need to take a leadership role by actively engaging with new technologies, designing new treatment processes and enhancing their medical skills with digital and AI competencies. The integration of digital skills into medical education and specialist training will be crucial for actively shaping the digital transformation and exploiting its full potential.
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Affiliation(s)
- Sebastian Kuhn
- Institut für Digitale Medizin, Philipps Universität Marburg und Universitätsklinikum Gießen und Marburg, 35042, Marburg, Deutschland.
| | - Johannes Knitza
- Institut für Digitale Medizin, Philipps Universität Marburg und Universitätsklinikum Gießen und Marburg, 35042, Marburg, Deutschland
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Du Y, Fang F. Transforming heart health: The emerging role of AI. Asian J Surg 2024; 47:2406-2407. [PMID: 38267271 DOI: 10.1016/j.asjsur.2024.01.075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 01/11/2024] [Indexed: 01/26/2024] Open
Affiliation(s)
- Yanyan Du
- Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Fang Fang
- Affiliated Hospital of Jining Medical University, Jining, Shandong, China.
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85
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Ma Y, Ma D, Xu X, Li J, Guan Z. Progress of MRI in predicting the circumferential resection margin of rectal cancer: A narrative review. Asian J Surg 2024; 47:2122-2131. [PMID: 38331609 DOI: 10.1016/j.asjsur.2024.01.131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/02/2024] [Accepted: 01/19/2024] [Indexed: 02/10/2024] Open
Abstract
Rectal cancer (RC) is the third most frequently diagnosed cancer worldwide, and the status of its circumferential resection margin (CRM) is of paramount significance for treatment strategies and prognosis. CRM involvement is defined as tumor touching or within 1 mm from the outermost part of tumor or outer border of the mesorectal or lymph node deposits to the resection margin. The incidence of involved CRM varied from 5.4 % to 36 %, which may associate with an in consistent definition of CRM, the quality of surgeries, and the different examination modalities. Although T and N status are essential factors in determining whether a patient should receive neoadjuvant therapy before surgery, CRM status is a powerful predictor of local and distant recurrence as well as survival rate. This review explores the significance of CRM, the various assessment methods, and the role of magnetic resonance imaging (MRI) and artificial intelligence-based MRI in predicting CRM status. MRI showed potential advantage in predicting CRM status with a high sensitivity and specificity compared to computed tomography (CT). We also discuss MRI advancements in RC imaging, including conventional MRI with body coil, high-resolution MRI with phased-array coil, and endorectal MRI. Along with a discussion of artificial intelligence-based MRI techniques to predict the CRM status of RCs before and after treatments.
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Affiliation(s)
- Yanqing Ma
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, China.
| | - Dongnan Ma
- Yangming College of Ningbo University, Ningbo, Zhejiang, 315010, China.
| | - Xiren Xu
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, China.
| | - Jie Li
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, China.
| | - Zheng Guan
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, China.
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Depeweg S, Rothkopf CA, Jäkel F. Solving Bongard Problems With a Visual Language and Pragmatic Constraints. Cogn Sci 2024; 48:e13432. [PMID: 38700123 DOI: 10.1111/cogs.13432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 02/15/2024] [Accepted: 02/26/2024] [Indexed: 05/05/2024]
Abstract
More than 50 years ago, Bongard introduced 100 visual concept learning problems as a challenge for artificial vision systems. These problems are now known as Bongard problems. Although they are well known in cognitive science and artificial intelligence, only very little progress has been made toward building systems that can solve a substantial subset of them. In the system presented here, visual features are extracted through image processing and then translated into a symbolic visual vocabulary. We introduce a formal language that allows representing compositional visual concepts based on this vocabulary. Using this language and Bayesian inference, concepts can be induced from the examples that are provided in each problem. We find a reasonable agreement between the concepts with high posterior probability and the solutions formulated by Bongard himself for a subset of 35 problems. While this approach is far from solving Bongard problems like humans, it does considerably better than previous approaches. We discuss the issues we encountered while developing this system and their continuing relevance for understanding visual cognition. For instance, contrary to other concept learning problems, the examples are not random in Bongard problems; instead they are carefully chosen to ensure that the concept can be induced, and we found it helpful to take the resulting pragmatic constraints into account.
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Affiliation(s)
| | - Contantin A Rothkopf
- Centre for Cognitive Science & Institute of Psychology, Technische Universität Darmstadt
- Frankfurt Institute for Advanced Studies, Frankfurt am Main
| | - Frank Jäkel
- Centre for Cognitive Science & Institute of Psychology, Technische Universität Darmstadt
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Papastratis I, Stergioulas A, Konstantinidis D, Daras P, Dimitropoulos K. Can ChatGPT provide appropriate meal plans for NCD patients? Nutrition 2024; 121:112291. [PMID: 38359704 DOI: 10.1016/j.nut.2023.112291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 10/30/2023] [Indexed: 02/17/2024]
Abstract
OBJECTIVES Dietary habits significantly affect health conditions and are closely related to the onset and progression of non-communicable diseases (NCDs). Consequently, a well-balanced diet plays an important role in lessening the effects of various disorders, including NCDs. Several artificial intelligence recommendation systems have been developed to propose healthy and nutritious diets. Most of these systems use expert knowledge and guidelines to provide tailored diets and encourage healthier eating habits. However, new advances in large language models such as ChatGPT, with their ability to produce human-like responses, have led individuals to search for advice in several tasks, including diet recommendations. This study aimed to determine the ability of ChatGPT models to generate appropriate personalized meal plans for patients with obesity, cardiovascular diseases, and type 2 diabetes. METHODS Using a state-of-the-art knowledge-based recommendation system as a reference, we assessed the meal plans generated by two large language models in terms of energy intake, nutrient accuracy, and meal variability. RESULTS Experimental results with different user profiles revealed the potential of ChatGPT models to provide personalized nutritional advice. CONCLUSION Additional supervision and guidance by nutrition experts or knowledge-based systems are required to ensure meal appropriateness for users with NCDs.
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Affiliation(s)
- Ilias Papastratis
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece.
| | - Andreas Stergioulas
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece
| | - Dimitrios Konstantinidis
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece
| | - Petros Daras
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece
| | - Kosmas Dimitropoulos
- The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece
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88
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Madadi Y, Delsoz M, Khouri AS, Boland M, Grzybowski A, Yousefi S. Applications of artificial intelligence-enabled robots and chatbots in ophthalmology: recent advances and future trends. Curr Opin Ophthalmol 2024; 35:238-243. [PMID: 38277274 PMCID: PMC10959691 DOI: 10.1097/icu.0000000000001035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
PURPOSE OF REVIEW Recent advances in artificial intelligence (AI), robotics, and chatbots have brought these technologies to the forefront of medicine, particularly ophthalmology. These technologies have been applied in diagnosis, prognosis, surgical operations, and patient-specific care in ophthalmology. It is thus both timely and pertinent to assess the existing landscape, recent advances, and trajectory of trends of AI, AI-enabled robots, and chatbots in ophthalmology. RECENT FINDINGS Some recent developments have integrated AI enabled robotics with diagnosis, and surgical procedures in ophthalmology. More recently, large language models (LLMs) like ChatGPT have shown promise in augmenting research capabilities and diagnosing ophthalmic diseases. These developments may portend a new era of doctor-patient-machine collaboration. SUMMARY Ophthalmology is undergoing a revolutionary change in research, clinical practice, and surgical interventions. Ophthalmic AI-enabled robotics and chatbot technologies based on LLMs are converging to create a new era of digital ophthalmology. Collectively, these developments portend a future in which conventional ophthalmic knowledge will be seamlessly integrated with AI to improve the patient experience and enhance therapeutic outcomes.
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Affiliation(s)
- Yeganeh Madadi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Mohammad Delsoz
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Albert S. Khouri
- Institute of Ophthalmology and Visual Science, University of Medicine and Dentistry of New Jersey, NJ, USA
| | - Michael Boland
- Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
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89
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Li L. Comment on: Artificial Intelligence-Based Total Mesorectal Excision Plane Navigation in Laparoscopic Colorectal Surgery. Dis Colon Rectum 2024; 67:e303. [PMID: 38319671 DOI: 10.1097/dcr.0000000000003284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Affiliation(s)
- Liqi Li
- Department of General Surgery, Xinqiao Hospital, Army Medical University Chongqing, China
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90
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Kian Ara H, Alemohammad N, Paymani Z, Ebrahimi M. Machine learning diagnosis of active Juvenile Idiopathic Arthritis on blood pool [ 99M Tc] Tc-MDP scintigraphy images. Nucl Med Commun 2024; 45:355-361. [PMID: 38312058 DOI: 10.1097/mnm.0000000000001822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
PURPOSE Neural network has widely been applied for medical classifications and disease diagnosis. This study employs deep learning to best discriminate Juvenile Idiopathic Arthritis (JIA), a pediatric chronic joint inflammatory disease, from healthy joints by exploring blood pool images of 2phase [ 99m Tc] Tc-MDP bone scintigraphy. METHODS Self-deigned multi-input Convolutional Neural Network (CNN) in addition to three available pre-trained models including VGG16, ResNet50 and Xception are applied on 1304 blood pool images of 326 healthy and known JIA children and adolescents (aged 1-16). RESULTS The self-designed model ROC analysis shows diagnostic efficiency with Area Under the Curve (AUC) 0.82 and 0.86 for knee and ankle joints, respectively. Among the three pertained models, VGG16 ROC analysis reveals AUC 0.76 and 0.81 for knee and ankle images, respectively. CONCLUSION The self-designed model shows best performance on blood pool scintigraph diagnosis of patients with JIA. VGG16 was the most efficient model rather to other pre-trained networks. This study can pave the way of artificial intelligence (AI) application in nuclear medicine for the diagnosis of pediatric inflammatory disease.
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91
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O'Connor S, Cave L, Philips N. Informing nursing policy: An exploration of digital health research by nurses in England. Int J Med Inform 2024; 185:105381. [PMID: 38402804 DOI: 10.1016/j.ijmedinf.2024.105381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 02/27/2024]
Abstract
AIMS Digital health technologies are designed, implemented, and evaluated to support clinical practice, enable patients to self-manage illness, and further public and global health. Nursing and health policies often emphasise the importance of evidence-based digital health services to deliver better care. However, the contribution nurses make to digital health research in many countries is unknown. Hence, this study aims to examine digital health research conducted by nurses in England. DESIGN A bibliometric analysis. METHODS The CINAHL, MEDLINE, and Scopus databases were searched between 2000 and 2022, and supplemented with a hand search of nurses' research profiles. Results were screened by title, abstract, and full text against eligibility criteria. Data were extracted and bibliometric analysis used to summarise the findings. RESULTS Mental health nurses produced the most digital health research in England, followed by nurses working in community care, with several disciplines underrepresented or missing. Web/online health services or information was the most researched technology, followed by mobile health and telehealth. Nurses based in the south-east and north-west of England produced the most digital health research, with other regions less well represented. CONCLUSION Nurse leaders should support nurses to conduct more digital health research by providing dedicated time, funding, and professional development opportunities, particularly in under researched clinical areas, technologies, and geographic regions to further evidence-based practice and patient care. More digital nursing data is needed to support nurse led research in areas like artificial intelligence and data science. The findings supported the national Philips Ives Review by identifying areas of digital nursing research that need more investment in England.
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Affiliation(s)
- Siobhan O'Connor
- Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London, United Kingdom.
| | - Louise Cave
- NHS England Transformation Directorate, NHS England, United Kingdom.
| | - Natasha Philips
- School of Health & Society, University of Salford, United Kingdom.
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92
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González Bermúdez A, Carramiñana D, Bernardos AM, Bergesio L, Besada JA. A fusion architecture to deliver multipurpose mobile health services. Comput Biol Med 2024; 173:108344. [PMID: 38574531 DOI: 10.1016/j.compbiomed.2024.108344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/13/2024] [Accepted: 03/17/2024] [Indexed: 04/06/2024]
Abstract
Mobile Health (mHealth) services typically make use of customized software architectures, leading to development-dependent fragmentation. Nevertheless, irrespective of their specific purpose, most mHealth services share common functionalities, where standard pieces could be reused or adapted to expedite service deployment and even extend the follow-up of appearing conditions under the same service. To harness compatibility and reuse, this article presents a data fusion architecture proposing a common design framework for mHealth services. An exhaustive mapping of mHealth functionalities identified in the literature serves as starting point. The architecture is then conceptualized making use of the Joint Directors of Laboratories (JDL) data fusion model. The aim of the architecture is to exploit the multi-source data acquisition capabilities supported by smartphones and Internet of Things devices, and artificial intelligence-enabled feature fusion. A series of interconnected fusion layers ensure streamlined data management; each layer is composed of microservices which may be implemented or omitted depending on the specific goals of the healthcare service. Moreover, the architecture considers essential features related to authentication mechanisms, data sharing protocols, practitioner-patient communication, context-based notifications and tailored visualization interfaces. The effectiveness of the architecture is underscored by its instantiation for four real cases, encompassing risk assessment for youth with mental health issues, remote monitoring for SARS-CoV-2 patients, liquid intake control for kidney disease patients, and peritoneal dialysis treatment support. This breadth of applications exemplifies how the architecture can effectively serve as a guidance framework to accelerate the design of mHealth services.
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Affiliation(s)
- Ana González Bermúdez
- Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, Spain.
| | - David Carramiñana
- Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, Spain
| | - Ana M Bernardos
- Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, Spain
| | - Luca Bergesio
- Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, Spain
| | - Juan A Besada
- Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, Spain
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93
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Noda R, Izaki Y, Kitano F, Komatsu J, Ichikawa D, Shibagaki Y. Performance of ChatGPT and Bard in self-assessment questions for nephrology board renewal. Clin Exp Nephrol 2024; 28:465-469. [PMID: 38353783 DOI: 10.1007/s10157-023-02451-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 12/25/2023] [Indexed: 04/23/2024]
Abstract
BACKGROUND Large language models (LLMs) have impacted advances in artificial intelligence. While LLMs have demonstrated high performance in general medical examinations, their performance in specialized areas such as nephrology is unclear. This study aimed to evaluate ChatGPT and Bard in their potential nephrology applications. METHODS Ninety-nine questions from the Self-Assessment Questions for Nephrology Board Renewal from 2018 to 2022 were presented to two versions of ChatGPT (GPT-3.5 and GPT-4) and Bard. We calculated the correct answer rates for the five years, each year, and question categories and checked whether they exceeded the pass criterion. The correct answer rates were compared with those of the nephrology residents. RESULTS The overall correct answer rates for GPT-3.5, GPT-4, and Bard were 31.3% (31/99), 54.5% (54/99), and 32.3% (32/99), respectively, thus GPT-4 significantly outperformed GPT-3.5 (p < 0.01) and Bard (p < 0.01). GPT-4 passed in three years, barely meeting the minimum threshold in two. GPT-4 demonstrated significantly higher performance in problem-solving, clinical, and non-image questions than GPT-3.5 and Bard. GPT-4's performance was between third- and fourth-year nephrology residents. CONCLUSIONS GPT-4 outperformed GPT-3.5 and Bard and met the Nephrology Board renewal standards in specific years, albeit marginally. These results highlight LLMs' potential and limitations in nephrology. As LLMs advance, nephrologists should understand their performance for future applications.
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Affiliation(s)
- Ryunosuke Noda
- Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan.
| | - Yuto Izaki
- Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Fumiya Kitano
- Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Jun Komatsu
- Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Daisuke Ichikawa
- Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Yugo Shibagaki
- Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-Ku, Kawasaki, Kanagawa, 216-8511, Japan
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94
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Calhoun BC, Uselman H, Olle EW. Development of Artificial Intelligence Image Classification Models for Determination of Umbilical Cord Vascular Anomalies. J Ultrasound Med 2024; 43:881-897. [PMID: 38279605 DOI: 10.1002/jum.16418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/05/2024] [Accepted: 01/07/2024] [Indexed: 01/28/2024]
Abstract
OBJECTIVE The goal of this work was to develop robust techniques for the processing and identification of SUA using artificial intelligence (AI) image classification models. METHODS Ultrasound images obtained retrospectively were analyzed for blinding, text removal, AI training, and image prediction. After developing and testing text removal methods, a small n-size study (40 images) using fastai/PyTorch to classify umbilical cord images. This data set was expanded to 286 lateral-CFI images that were used to compare: different neural network performance, diagnostic value, and model predictions. RESULTS AI-Optical Character Recognition method was superior in its ability to remove text from images. The small n-size mixed single umbilical artery determination data set was tested with a pretrained ResNet34 neural network and obtained and error rate average of 0.083 (n = 3). The expanded data set was then tested with several AI models. The majority of the tested networks were able to obtain an average error rate of <0.15 with minimal modifications. The ResNet34-default performed the best with: an image-classification error rate of 0.0175, sensitivity of 1.00, specificity of 0.97, and ability to correctly infer classification. CONCLUSION This work provides a robust framework for ultrasound image AI classifications. AI could successfully classify umbilical cord types of ultrasound image study with excellent diagnostic value. Together this study provides a reproducible framework to develop AI-specific ultrasound classification of umbilical cord or other diagnoses to be used in conjunction with physicians for optimal patient care.
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Affiliation(s)
- Byron C Calhoun
- Department of Obstetrics and Gynecology, WVU School of Medicine, Charleston Division, Charleston, West Virginia, USA
- Maternal-Fetal Medicine, WVU School of Medicine, Charleston Division, Charleston, West Virginia, USA
| | - Heather Uselman
- Resident Department of Obstetrics and Gynecology, Charleston Area Medical Center, Charleston, West Virginia, USA
| | - Eric W Olle
- Research and Development, SynXBio Inc., Charleston, West Virginia, USA
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95
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Tournois L, Trousset V, Hatsch D, Delabarde T, Ludes B, Lefèvre T. Artificial intelligence in the practice of forensic medicine: a scoping review. Int J Legal Med 2024; 138:1023-1037. [PMID: 38087052 PMCID: PMC11003914 DOI: 10.1007/s00414-023-03140-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 11/21/2023] [Indexed: 04/11/2024]
Abstract
Forensic medicine is a thriving application field for artificial intelligence (AI). Indeed, AI applications intended to forensic pathologists or forensic physicians have emerged since the last decade. For example, AI models were developed to help estimate the biological age of migrants or human remains. However, the uses of AI applications by forensic pathologists or physicians and their levels of integration in medicolegal practices are not well described yet. Therefore, a scoping review was conducted on PubMed, ScienceDirect, and Scopus databases. This review included articles that mention any AI application used by forensic pathologists or physicians in practice or any AI model applied in one expertise field of the forensic pathologist or physician. Articles in other languages than English or French or dealing mainly with complementary analyses handled by experts who are not forensic pathologists or physicians or with AI to analyze data for research purposes in forensic medicine were excluded from this review. All the relevant information was retrieved in each article from a grid analysis derived and adapted from the TRIPOD checklist. This review included 35 articles and revealed that AI applications are developed in thanatology and in clinical forensic medicine. However, those applications seem to mainly remain in research and development stages. Indeed, the use of AI applications by forensic pathologists or physicians is not actual due to issues discussed in this article. Finally, the integration of AI in daily medicolegal practice involves not only forensic pathologists or physicians but also legal professionals.
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Affiliation(s)
- Laurent Tournois
- Université Paris Cité, CNRS UMR 8045, 75006, Paris, France.
- BioSilicium, Riom, France.
| | - Victor Trousset
- IRIS Institut de Recherche Interdisciplinaire Sur Les Enjeux Sociaux, UMR8156 CNRS - U997 Inserm - EHESS - Université Sorbonne Paris Nord, Paris, France
- Department of Forensic and Social Medicine, AP-HP, Jean Verdier Hospital, Bondy, France
| | | | - Tania Delabarde
- Université Paris Cité, CNRS UMR 8045, 75006, Paris, France
- Institut Médico-Légal de Paris, Paris, France
| | - Bertrand Ludes
- Université Paris Cité, CNRS UMR 8045, 75006, Paris, France
- Institut Médico-Légal de Paris, Paris, France
| | - Thomas Lefèvre
- IRIS Institut de Recherche Interdisciplinaire Sur Les Enjeux Sociaux, UMR8156 CNRS - U997 Inserm - EHESS - Université Sorbonne Paris Nord, Paris, France
- Department of Forensic and Social Medicine, AP-HP, Jean Verdier Hospital, Bondy, France
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96
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Burke EG, Rosengart TK. Invited Commentary: Artificial Intelligence to the Rescue: The Unending Search for Retained Surgical Items. J Am Coll Surg 2024; 238:860-861. [PMID: 38288939 DOI: 10.1097/xcs.0000000000000931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
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97
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González R, Poenaru D, Woo R, Trappey AF, Carter S, Darcy D, Encisco E, Gulack B, Miniati D, Tombash E, Huang EY. ChatGPT: What Every Pediatric Surgeon Should Know About Its Potential Uses and Pitfalls. J Pediatr Surg 2024; 59:941-947. [PMID: 38336588 DOI: 10.1016/j.jpedsurg.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/30/2023] [Accepted: 01/09/2024] [Indexed: 02/12/2024]
Abstract
ChatGPT - currently the most popular generative artificial intelligence system - has been revolutionizing the world and healthcare since its release in November 2022. ChatGPT is a conversational chatbot that uses machine learning algorithms to enhance its replies based on user interactions and is a part of a broader effort to develop natural language processing that can assist people in their daily lives by understanding and responding to human language in a useful and engaging way. Thus far, many potential applications within healthcare have been described, despite its relatively recent release. This manuscript offers the pediatric surgical community a primer on this new technology and discusses some initial observations about its potential uses and pitfalls. Moreover, it introduces the perspectives of medical journals and surgical societies regarding the use of this artificial intelligence chatbot. As ChatGPT and other large language models continue to evolve, it is the responsibility of the pediatric surgery community to stay abreast of these changes and play an active role in safely incorporating them into our field for the benefit of our patients. LEVEL OF EVIDENCE: V.
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Affiliation(s)
- Raquel González
- Division of Pediatric Surgery, Johns Hopkins All Children's Hospital, 501 6th Avenue S, Saint Petersburg, FL, 33701, USA.
| | - Dan Poenaru
- McGill University, 5252 Boul. De Maissonneuve O. rm. 3E.05, Montréal, QC, H4a 3S5, Canada
| | - Russell Woo
- Department of Surgery, Division of Pediatric Surgery, University of Hawai'i, John A. Burns School of Medicine, 1319 Punahou Street, Suite 600, Honolulu, HI, 96826, USA
| | - A Francois Trappey
- Pediatric General and Thoracic Surgery, Brooke Army Medical Center, 3551 Roger Brooke Dr, Fort Sam Houston, TX, 78234, USA
| | - Stewart Carter
- Division of Pediatric Surgery, University of Louisville, Norton Children's Hospital, 315 East Broadway, Suite 565, Louisville, KY, 40202, USA
| | - David Darcy
- Golisano Children's Hospital, University of Rochester Medical Center, 601 Elmwood Avenue, Box SURG, Rochester, NY, 14642, USA
| | - Ellen Encisco
- Division of Pediatric General and Thoracic Surgery, Cincinnati Children's Hospital, 3333 Burnet Ave, Cincinnati, OH, 45229, USA
| | - Brian Gulack
- Rush University Medical Center, 1653 W Congress Parkway, Kellogg, Chicago, IL, 60612, USA
| | - Doug Miniati
- Department of Pediatric Surgery, Kaiser Permanente Roseville, 1600 Eureka Road, Building C, Suite C35, Roseville, CA, 95661, USA
| | - Edzhem Tombash
- Division of Pediatric General and Thoracic Surgery, Cincinnati Children's Hospital, 3333 Burnet Ave, Cincinnati, OH, 45229, USA
| | - Eunice Y Huang
- Vanderbilt University Medical Center, Monroe Carell Jr. Children's Hospital, 2200 Children's Way, Suite 7100, Nashville, TN, 37232, USA
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98
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Posch C. Melanocytic lesions: How to navigate variations in human and artificial intelligence. J Eur Acad Dermatol Venereol 2024; 38:792-793. [PMID: 38661016 DOI: 10.1111/jdv.19950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 02/29/2024] [Indexed: 04/26/2024]
Affiliation(s)
- Christian Posch
- Department of Dermatology, Clinic Hietzing, Vienna Healthcare Group, Vienna, Austria
- School of Medicine, Sigmund Freud University, Vienna, Austria
- Department of Dermatology and Allergy, School of Medicine, German Cancer Consortium (DKTK), Technical University of Munich, Munich, Germany
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99
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Garcés-Jiménez A, Polo-Luque ML, Gómez-Pulido JA, Rodríguez-Puyol D, Gómez-Pulido JM. Predictive health monitoring: Leveraging artificial intelligence for early detection of infectious diseases in nursing home residents through discontinuous vital signs analysis. Comput Biol Med 2024; 174:108469. [PMID: 38636331 DOI: 10.1016/j.compbiomed.2024.108469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 04/20/2024]
Abstract
This research addresses the problem of detecting acute respiratory, urinary tract, and other infectious diseases in elderly nursing home residents using machine learning algorithms. The study analyzes data extracted from multiple vital signs and other contextual information for diagnostic purposes. The daily data collection process encounters sampling constraints due to weekends, holidays, shift changes, staff turnover, and equipment breakdowns, resulting in numerous nulls, repeated readings, outliers, and meaningless values. The short time series generated also pose a challenge to analysis, preventing the extraction of seasonal information or consistent trends. Blind data collection results in most of the data coming from periods when residents are healthy, resulting in excessively imbalanced data. This study proposes a data cleaning process and then builds a mechanism that reproduces the basal activity of the residents to improve the classification of the disease. The results show that the proposed basal module-assisted machine learning techniques allow anticipating diagnostics 2, 3 or 4 days before doctors decide to start treatment with antibiotics, achieving a performance measured by the area-under-the-curve metric of 0.857. The contributions of this work are: (1) a new data cleaning process; (2) the analysis of contextual information to improve data quality; (3) the generation of a baseline measure for relative comparison; and (4) the use of either binary (disease/no disease) or multiclass classification, differentiating among types of infections and showing the advantages of multiclass versus binary classification. From a medical point of view, the anticipated detection of infectious diseases in institutionalized individuals is brand new.
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Affiliation(s)
- Alberto Garcés-Jiménez
- Department of Computer Science, Universidad de Alcalá, Politechnic School, Alcala de Henares, 28805, Spain
| | - María-Luz Polo-Luque
- Department of Nursing and Physiotherapy, Universidad de Alcalá, Faculty of Medicine and Health Sciences, Alcala de Henares, 28805, Spain
| | - Juan A Gómez-Pulido
- Department of Technologies of Computers and Communications, Universidad de Extremadura, School of Technology, Cáceres, 10003, Spain.
| | - Diego Rodríguez-Puyol
- Department of Medicine and Medical Specialties, Research Foundation of the University Hospital Príncipe de Asturias, Campus Científico Tecnológico, Alcala de Henares, 28805, Spain
| | - José M Gómez-Pulido
- Department of Computer Science, Universidad de Alcalá, Politechnic School, Alcala de Henares, 28805, Spain
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100
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Gao Z. [The assessment of facial nerve function]. Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2024; 38:359-363. [PMID: 38686468 DOI: 10.13201/j.issn.2096-7993.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Indexed: 05/02/2024]
Abstract
The assessment of facial nerve function plays a crucial role in the diagnosis and treatment of facial nerve disorders.The assessment system for facial nerve function is primarily categorized into subjective and objective systems.While the subjective assessment system is relatively simple, it lacks accuracy as it can be influenced by the subjectivity of evaluator.Whereas, the objective system offers higher precision and stability, providing more quantitative information. In recent years, benefited with advancements in computer vision and artificial intelligence,we have witnessed increasingly accurate,stable and intelligent facial nerve assessment systems gradually implemented in clinical practice.When selecting a specific facial nerve assessment system,factors such as clinical scenarios,assessment objectives,patient characteristics should be considered.
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Affiliation(s)
- Zhiqiang Gao
- Department of Otorhinolaryngology,Peking Union Medical College Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing,100010,China
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