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Borghetti P, Costantino G, Santoro V, Mataj E, Singh N, Vitali P, Greco D, Volpi G, Sepulcri M, Guida C, Tomasi C, Buglione M, Nardone V. Artificial Intelligence-suggested Predictive Model of Survival in Patients Treated With Stereotactic Radiotherapy for Early Lung Cancer. In Vivo 2024; 38:1359-1366. [PMID: 38688600 PMCID: PMC11059897 DOI: 10.21873/invivo.13576] [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: 10/03/2023] [Revised: 01/30/2024] [Accepted: 02/13/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND/AIM Overall survival (OS)-predictive models to clinically stratify patients with stage I Non-Small Cell Lung Cancer (NSCLC) undergoing stereotactic body radiation therapy (SBRT) are still unavailable. The aim of this work was to build a predictive model of OS in this setting. PATIENTS AND METHODS Clinical variables of patients treated in three Institutions with SBRT for stage I NSCLC were retrospectively collected into a reference cohort A (107 patients) and 2 comparative cohorts B1 (32 patients) and B2 (38 patients). A predictive model was built using Cox regression (CR) and artificial neural networks (ANN) on reference cohort A and then tested on comparative cohorts. RESULTS Cohort B1 patients were older and with worse chronic obstructive pulmonary disease (COPD) than cohort A. Cohort B2 patients were heavier smokers but had lower Charlson Comorbidity Index (CCI). At CR analysis for cohort A, only ECOG Performance Status 0-1 and absence of previous neoplasms correlated with better OS. The model was enhanced combining ANN and CR findings. The reference cohort was divided into prognostic Group 1 (0-2 score) and Group 2 (3-9 score) to assess model's predictions on OS: grouping was close to statistical significance (p=0.081). One and 2-year OS resulted higher for Group 1, lower for Group 2. In comparative cohorts, the model successfully predicted two groups of patients with divergent OS trends: higher for Group 1 and lower for Group 2. CONCLUSION The produced model is a relevant tool to clinically stratify SBRT candidates into prognostic groups, even when applied to different cohorts. ANN are a valuable resource, providing useful data to build a prognostic model that deserves to be validated prospectively.
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Affiliation(s)
- Paolo Borghetti
- Radiation Oncology Department, Spedali Civili and University of Brescia, Brescia, Italy
| | | | - Valeria Santoro
- Azienda Ospedaliera Universitaria Integrata Verona, Radiation Oncology, Verona, Italy
| | - Eneida Mataj
- Radiation Oncology Department, Spedali Civili and University of Brescia, Brescia, Italy;
| | - Navdeep Singh
- Radiation Oncology Department, Spedali Civili and University of Brescia, Brescia, Italy
| | - Paola Vitali
- Radiation Oncology Department, Spedali Civili and University of Brescia, Brescia, Italy
| | - Diana Greco
- Radiation Oncology Department, Spedali Civili and University of Brescia, Brescia, Italy
| | - Giulia Volpi
- Azienda Ospedaliera Universitaria Integrata Verona, Radiation Oncology, Verona, Italy
| | - Matteo Sepulcri
- Radiotherapy Unit, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy
| | - Cesare Guida
- Radiotherapy Unit, Ospedale del Mare, ASL Napoli 1, Naples, Italy
| | | | - Michela Buglione
- Radiation Oncology Department, Spedali Civili and University of Brescia, Brescia, Italy
| | - Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
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Ketchem CJ, Lynch KL, Chang JW, Dellon ES. Artificial Intelligence Chatbot Shows Multiple Inaccuracies When Responding to Questions About Eosinophilic Esophagitis. Clin Gastroenterol Hepatol 2024; 22:1133-1135. [PMID: 37879522 PMCID: PMC11039562 DOI: 10.1016/j.cgh.2023.10.004] [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] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 09/16/2023] [Accepted: 10/02/2023] [Indexed: 10/27/2023]
Affiliation(s)
| | - Kristle L Lynch
- Division of Gastroenterology, Department of Medicine, Hospital of the University of Pennsylvania, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Joy W Chang
- Division of Gastroenterology, Department of Medicine, University of Michigan, Ann Arbor, Michigan
| | - Evan S Dellon
- Center for Esophageal Diseases and Swallowing, Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina; Center for Gastrointestinal Biology and Disease, Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina.
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Yıldız M, Sarpdağı Y, Okuyar M, Yildiz M, Çiftci N, Elkoca A, Yildirim MS, Aydin MA, Parlak M, Bingöl B. Segmentation and classification of skin burn images with artificial intelligence: Development of a mobile application. Burns 2024; 50:966-979. [PMID: 38331663 DOI: 10.1016/j.burns.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] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 12/26/2023] [Accepted: 01/10/2024] [Indexed: 02/10/2024]
Abstract
AIM This study was conducted to determine the segmentation, classification, object detection, and accuracy of skin burn images using artificial intelligence and a mobile application. With this study, individuals were able to determine the degree of burns and see how to intervene through the mobile application. METHODS This research was conducted between 26.10.2021-01.09.2023. In this study, the dataset was handled in two stages. In the first stage, the open-access dataset was taken from https://universe.roboflow.com/, and the burn images dataset was created. In the second stage, in order to determine the accuracy of the developed system and artificial intelligence model, the patients admitted to the hospital were identified with our own design Burn Wound Detection Android application. RESULTS In our study, YOLO V7 architecture was used for segmentation, classification, and object detection. There are 21018 data in this study, and 80% of them are used as training data, and 20% of them are used as test data. The YOLO V7 model achieved a success rate of 75.12% on the test data. The Burn Wound Detection Android mobile application that we developed in the study was used to accurately detect images of individuals. CONCLUSION In this study, skin burn images were segmented, classified, object detected, and a mobile application was developed using artificial intelligence. First aid is crucial in burn cases, and it is an important development for public health that people living in the periphery can quickly determine the degree of burn through the mobile application and provide first aid according to the instructions of the mobile application.
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Affiliation(s)
- Metin Yıldız
- Department of Nursing, Sakarya University, Sakarya, Turkey.
| | - Yakup Sarpdağı
- Department of Nursing Van Yuzuncu Yil University, Turkey
| | - Mehmet Okuyar
- Sakarya University of Applied Sciences Biomedical Engineering, Sakarya, Turkey
| | - Mehmet Yildiz
- Sakarya University of Applied Sciences, Distance Education Research and Application Center, Sakarya, Turkey
| | - Necmettin Çiftci
- Muş Alparslan University, Faculty of Health Sciences, Department of Nursing, 49100 Muş, Turkey
| | - Ayşe Elkoca
- Gaziantep Islamic University of Science and Technology Faculty of Health Sciences, Midwifery, Turkey
| | - Mehmet Salih Yildirim
- Vocational School of Health Services, Agri Ibrahim Cecen University School of Health, Agri, Turkey
| | | | - Mehmet Parlak
- Ataturk University, Department of Nursing, Erzurum, Turkey
| | - Bünyamin Bingöl
- Sakarya University, Electrical and Electronics Engineering, Sakarya, Turkey
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154
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Zhu L, Lai Y, Mou W, Zhang H, Lin A, Qi C, Yang T, Xu L, Zhang J, Luo P. ChatGPT's ability to generate realistic experimental images poses a new challenge to academic integrity. J Hematol Oncol 2024; 17:27. [PMID: 38693553 PMCID: PMC11064365 DOI: 10.1186/s13045-024-01543-8] [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: 03/26/2024] [Accepted: 04/08/2024] [Indexed: 05/03/2024] Open
Abstract
The rapid advancements in large language models (LLMs) such as ChatGPT have raised concerns about their potential impact on academic integrity. While initial concerns focused on ChatGPT's writing capabilities, recent updates have integrated DALL-E 3's image generation features, extending the risks to visual evidence in biomedical research. Our tests revealed ChatGPT's nearly barrier-free image generation feature can be used to generate experimental result images, such as blood smears, Western Blot, immunofluorescence and so on. Although the current ability of ChatGPT to generate experimental images is limited, the risk of misuse is evident. This development underscores the need for immediate action. We suggest that AI providers restrict the generation of experimental image, develop tools to detect AI-generated images, and consider adding "invisible watermarks" to the generated images. By implementing these measures, we can better ensure the responsible use of AI technology in academic research and maintain the integrity of scientific evidence.
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Affiliation(s)
- Lingxuan Zhu
- Department of Oncology, Zhujiang Hospital, Southern Medical University, 253 Industrial Avenue, 510282, Guangzhou, Guangdong, China
| | - Yancheng Lai
- Department of Oncology, Zhujiang Hospital, Southern Medical University, 253 Industrial Avenue, 510282, Guangzhou, Guangdong, China
| | - Weiming Mou
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haoran Zhang
- Department of Oncology, Zhujiang Hospital, Southern Medical University, 253 Industrial Avenue, 510282, Guangzhou, Guangdong, China
| | - Anqi Lin
- Department of Oncology, Zhujiang Hospital, Southern Medical University, 253 Industrial Avenue, 510282, Guangzhou, Guangdong, China
| | - Chang Qi
- Institute of Logic and Computation, TU Wien, Wien, Austria
| | - Tao Yang
- Department of Medical Oncology, National Clinical Research Center for Cancer /Cancer Hospital, National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liling Xu
- Department of Oncology, Zhujiang Hospital, Southern Medical University, 253 Industrial Avenue, 510282, Guangzhou, Guangdong, China
| | - Jian Zhang
- Department of Oncology, Zhujiang Hospital, Southern Medical University, 253 Industrial Avenue, 510282, Guangzhou, Guangdong, China.
| | - Peng Luo
- Department of Oncology, Zhujiang Hospital, Southern Medical University, 253 Industrial Avenue, 510282, Guangzhou, Guangdong, China.
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León-Domínguez U. Potential cognitive risks of generative transformer-based AI chatbots on higher order executive functions. Neuropsychology 2024; 38:293-308. [PMID: 38300581 DOI: 10.1037/neu0000948] [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: 02/02/2024] Open
Abstract
BACKGROUND Chat generative retrained transformer (ChatGPT) represents a groundbreaking advancement in Artificial Intelligence (AI-chatbot) technology, utilizing transformer algorithms to enhance natural language processing and facilitating their use for addressing specific tasks. These AI chatbots can respond to questions by generating verbal instructions similar to those a person would provide during the problem-solving process. AIM ChatGPT has become the fastest growing software in terms of user adoption in history, leading to an anticipated widespread use of this technology in the general population. Current literature is predominantly focused on the functional aspects of these technologies, but the field has not yet explored hypotheses on how these AI chatbots could impact the evolutionary aspects of human cognitive development. Thesis: The "neuronal recycling hypothesis" posits that the brain undergoes structural transformation by incorporating new cultural tools into "neural niches," consequently altering individual cognition. In the case of technological tools, it has been established that they reduce the cognitive demand needed to solve tasks through a process called "cognitive offloading." In this theoretical article, three hypotheses were proposed via forward inference about how algorithms such as ChatGPT and similar models may influence the cognitive processes and structures of upcoming generations. CONCLUSIONS By forecasting the neurocognitive effects of these technologies, educational and political communities can anticipate future scenarios and formulate strategic plans to either mitigate or enhance the cognitive influence that these factors may have on the general population. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Kekatpure A, Kekatpure A, Deshpande S, Srivastava S. Development of a diagnostic support system for distal humerus fracture using artificial intelligence. Int Orthop 2024; 48:1303-1311. [PMID: 38499714 DOI: 10.1007/s00264-024-06125-4] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 02/18/2024] [Indexed: 03/20/2024]
Abstract
PURPOSE AI has shown promise in automating and improving various tasks, including medical image analysis. Distal humerus fractures are a critical clinical concern that requires early diagnosis and treatment to avoid complications. The standard diagnostic method involves X-ray imaging, but subtle fractures can be missed, leading to delayed or incorrect diagnoses. Deep learning, a subset of artificial intelligence, has demonstrated the ability to automate medical image analysis tasks, potentially improving fracture identification accuracy and reducing the need for additional and cost-intensive imaging modalities (Schwarz et al. 2023). This study aims to develop a deep learning-based diagnostic support system for distal humerus fractures using conventional X-ray images. The primary objective of this study is to determine whether deep learning can provide reliable image-based fracture detection recommendations for distal humerus fractures. METHODS Between March 2017 and March 2022, our tertiary hospital's PACS data were evaluated for conventional radiography images of the anteroposterior (AP) and lateral elbow for suspected traumatic distal humerus fractures. The data set consisted of 4931 images of patients seven years and older, after excluding paediatric images below seven years due to the absence of ossification centres. Two senior orthopaedic surgeons with 12 + years of experience reviewed and labelled the images as fractured or normal. The data set was split into training sets (79.88%) and validation tests (20.1%). Image pre-processing was performed by cropping the images to 224 × 224 pixels around the capitellum, and the deep learning algorithm architecture used was ResNet18. RESULTS The deep learning model demonstrated an accuracy of 69.14% in the validation test set, with a specificity of 95.89% and a positive predictive value (PPV) of 99.47%. However, the sensitivity was 61.49%, indicating that the model had a relatively high false negative rate. ROC analysis showed an AUC of 0.787 when deep learning AI was the reference and an AUC of 0.580 when the most senior orthopaedic surgeon was the reference. The performance of the model was compared with that of other orthopaedic surgeons of varying experience levels, showing varying levels of diagnostic precision. CONCLUSION The developed deep learning-based diagnostic support system shows potential for accurately diagnosing distal humerus fractures using AP and lateral elbow radiographs. The model's specificity and PPV indicate its ability to mark out occult lesions and has a high false positive rate. Further research and validation are necessary to improve the sensitivity and diagnostic accuracy of the model for practical clinical implementation.
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157
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Liu CM, Chen WS, Chang SL, Hsieh YC, Hsu YH, Chang HX, Lin YJ, Lo LW, Hu YF, Chung FP, Chao TF, Tuan TC, Liao JN, Lin CY, Chang TY, Kuo L, Wu CI, Wu MH, Chen CK, Chang YY, Shiu YC, Lu HHS, Chen SA. Use of artificial intelligence and I-Score for prediction of recurrence before catheter ablation of atrial fibrillation. Int J Cardiol 2024; 402:131851. [PMID: 38360099 DOI: 10.1016/j.ijcard.2024.131851] [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: 11/13/2023] [Revised: 01/14/2024] [Accepted: 02/10/2024] [Indexed: 02/17/2024]
Abstract
BACKGROUND Based solely on pre-ablation characteristics, previous risk scores have demonstrated variable predictive performance. This study aimed to predict the recurrence of AF after catheter ablation by using artificial intelligence (AI)-enabled pre-ablation computed tomography (PVCT) images and pre-ablation clinical data. METHODS A total of 638 drug-refractory paroxysmal atrial fibrillation (AF) patients undergone ablation were recruited. For model training, we used left atria (LA) acquired from pre-ablation PVCT slices (126,288 images). A total of 29 clinical variables were collected before ablation, including baseline characteristics, medical histories, laboratory results, transthoracic echocardiographic parameters, and 3D reconstructed LA volumes. The I-Score was applied to select variables for model training. For the prediction of one-year AF recurrence, PVCT deep-learning and clinical variable machine-learning models were developed. We then applied machine learning to ensemble the PVCT and clinical variable models. RESULTS The PVCT model achieved an AUC of 0.63 in the test set. Various combinations of clinical variables selected by I-Score can yield an AUC of 0.72, which is significantly better than all variables or features selected by nonparametric statistics (AUCs of 0.66 to 0.69). The ensemble model (PVCT images and clinical variables) significantly improved predictive performance up to an AUC of 0.76 (sensitivity of 86.7% and specificity of 51.0%). CONCLUSIONS Before ablation, AI-enabled PVCT combined with I-Score features was applicable in predicting recurrence in paroxysmal AF patients. Based on all possible predictors, the I-Score is capable of identifying the most influential combination.
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Affiliation(s)
- Chih-Min Liu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Wei-Shiang Chen
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Shih-Lin Chang
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Cheng Hsieh
- Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Yuan-Heng Hsu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Hao-Xiang Chang
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yenn-Jiang Lin
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Li-Wei Lo
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Feng Hu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Fa-Po Chung
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tze-Fan Chao
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ta-Chuan Tuan
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jo-Nan Liao
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chin-Yu Lin
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ting-Yung Chang
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ling Kuo
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Cheng-I Wu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Mei-Han Wu
- Department of Medical Imaging, Diagnostic Radiology, Cheng Hsin General Hospital, Taipei, Taiwan
| | - Chun-Ku Chen
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Ying-Yueh Chang
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yang-Che Shiu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan
| | - Henry Horng-Shing Lu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA.
| | - Shih-Ann Chen
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan; National Chung Hsing University, Taichung, Taiwan
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Johnston TH, Lacoste AMB, Ravenscroft P, Su J, Tamadon S, Seifi M, Lang AE, Fox SH, Brotchie JM, Visanji NP. Using artificial intelligence to identify drugs for repurposing to treat l-DOPA-induced dyskinesia. Neuropharmacology 2024; 248:109880. [PMID: 38412888 DOI: 10.1016/j.neuropharm.2024.109880] [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: 06/14/2023] [Revised: 02/02/2024] [Accepted: 02/18/2024] [Indexed: 02/29/2024]
Abstract
Repurposing regulatory agency-approved molecules, with proven safety in humans, is an attractive option for developing new treatments for disease. We identified and assessed the efficacy of 3 drugs predicted by an in silico screen as having the potential to treat l-DOPA-induced dyskinesia (LID) in Parkinson's disease. We analysed ∼1.3 million Medline abstracts using natural language processing and ranked 3539 existing drugs based on predicted ability to reduce LID. 3 drugs from the top 5% of the 3539 candidates; lorcaserin, acamprosate and ganaxolone, were prioritized for preclinical testing based on i) having a novel mechanism of action, ii) having not been previously validated for the treatment of LID, iii) being blood-brain-barrier penetrant and orally bioavailable and iv) being clinical trial ready. We assessed the efficacy of acamprosate, ganaxolone and lorcaserin in a rodent model of l-DOPA-induced hyperactivity, with lorcaserin affording a 58% reduction in rotational asymmetry (P < 0.05) compared to vehicle. Acamprosate and ganaxolone failed to demonstrate efficacy. Lorcaserin, a 5HT2C agonist, was then further tested in MPTP lesioned dyskinetic macaques where it afforded an 82% reduction in LID (P < 0.05), unfortunately accompanied by a significant increase in parkinsonian disability. In conclusion, although our data do not support the repurposing of lorcaserin, acamprosate or ganaxolone per se for LID, we demonstrate value of an in silico approach to identify candidate molecules which, in combination with an in vivo screen, can facilitate clinical development decisions. The present study adds to a growing literature in support of this paradigm shifting approach in the repurposing pipeline.
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Affiliation(s)
- Tom H Johnston
- Atuka Inc, Suite 5600, 100 King St. W. Toronto, Ontario, M5X 1C9, Canada
| | | | - Paula Ravenscroft
- Atuka Inc, Suite 5600, 100 King St. W. Toronto, Ontario, M5X 1C9, Canada
| | - Jin Su
- Atuka Inc, Suite 5600, 100 King St. W. Toronto, Ontario, M5X 1C9, Canada
| | - Sahar Tamadon
- Atuka Inc, Suite 5600, 100 King St. W. Toronto, Ontario, M5X 1C9, Canada
| | - Mahtab Seifi
- Atuka Inc, Suite 5600, 100 King St. W. Toronto, Ontario, M5X 1C9, Canada
| | - Anthony E Lang
- Krembil Brain Institute, 60 Leonard Avenue, Toronto, ON, M5T 0S8, Canada; Edmond J Safra Program in Parkinson Disease, Parkinson Foundation Centre of Excellence, Toronto Western Hospital, 399, Bathurst St, Toronto, ON, M5T 2S8, Canada
| | - Susan H Fox
- Krembil Brain Institute, 60 Leonard Avenue, Toronto, ON, M5T 0S8, Canada; Edmond J Safra Program in Parkinson Disease, Parkinson Foundation Centre of Excellence, Toronto Western Hospital, 399, Bathurst St, Toronto, ON, M5T 2S8, Canada
| | - Jonathan M Brotchie
- Krembil Brain Institute, 60 Leonard Avenue, Toronto, ON, M5T 0S8, Canada; Atuka Inc, Suite 5600, 100 King St. W. Toronto, Ontario, M5X 1C9, Canada
| | - Naomi P Visanji
- Krembil Brain Institute, 60 Leonard Avenue, Toronto, ON, M5T 0S8, Canada; Edmond J Safra Program in Parkinson Disease, Parkinson Foundation Centre of Excellence, Toronto Western Hospital, 399, Bathurst St, Toronto, ON, M5T 2S8, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada; Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada.
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159
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Momenaei B, Mansour HA, Kuriyan AE, Xu D, Sridhar J, Ting DSW, Yonekawa Y. ChatGPT enters the room: what it means for patient counseling, physician education, academics, and disease management. Curr Opin Ophthalmol 2024; 35:205-209. [PMID: 38334288 DOI: 10.1097/icu.0000000000001036] [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: 02/10/2024]
Abstract
PURPOSE OF REVIEW This review seeks to provide a summary of the most recent research findings regarding the utilization of ChatGPT, an artificial intelligence (AI)-powered chatbot, in the field of ophthalmology in addition to exploring the limitations and ethical considerations associated with its application. RECENT FINDINGS ChatGPT has gained widespread recognition and demonstrated potential in enhancing patient and physician education, boosting research productivity, and streamlining administrative tasks. In various studies examining its utility in ophthalmology, ChatGPT has exhibited fair to good accuracy, with its most recent iteration showcasing superior performance in providing ophthalmic recommendations across various ophthalmic disorders such as corneal diseases, orbital disorders, vitreoretinal diseases, uveitis, neuro-ophthalmology, and glaucoma. This proves beneficial for patients in accessing information and aids physicians in triaging as well as formulating differential diagnoses. Despite such benefits, ChatGPT has limitations that require acknowledgment including the potential risk of offering inaccurate or harmful information, dependence on outdated data, the necessity for a high level of education for data comprehension, and concerns regarding patient privacy and ethical considerations within the research domain. SUMMARY ChatGPT is a promising new tool that could contribute to ophthalmic healthcare education and research, potentially reducing work burdens. However, its current limitations necessitate a complementary role with human expert oversight.
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Affiliation(s)
- Bita Momenaei
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Hana A Mansour
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Ajay E Kuriyan
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - David Xu
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Jayanth Sridhar
- University of California Los Angeles, Los Angeles, California, USA
| | | | - Yoshihiro Yonekawa
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
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Zhang X, Xiao J, Yang F, Qu H, Ye C, Chen S, Guo Y. Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning. Int J Legal Med 2024; 138:1139-1148. [PMID: 38047927 DOI: 10.1007/s00414-023-03118-7] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/25/2023] [Indexed: 12/05/2023]
Abstract
OBJECTIVE The aim of this study is to identify a rapid, sensitive, and non-destructive auxiliary approach for postmortem diagnosis of SCD, addressing the challenges faced in forensic practice. METHODS ATR-FTIR spectroscopy was employed to collect spectral features of blood samples from different cases, combined with pathological changes. Mixed datasets were analyzed using ANN, KNN, RF, and SVM algorithms. Evaluation metrics such as accuracy, precision, recall, F1-score and confusion matrix were used to select the optimal algorithm and construct the postmortem diagnosis model for SCD. RESULTS A total of 77 cases were collected, including 43 cases in the SCD group and 34 cases in the non-SCD group. A total of 693 spectrogram were obtained. Compared to other algorithms, the SVM algorithm demonstrated the highest accuracy, reaching 95.83% based on spectral biomarkers. Furthermore, by combing spectral biomarkers with age, gender, and cardiac histopathological changes, the accuracy of the SVM model could get 100%. CONCLUSION Integrating artificial intelligence technology, pathology, and physical chemistry analysis of blood components can serve as an effective auxiliary method for postmortem diagnosis of SCD.
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Affiliation(s)
- Xiangyan Zhang
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China
| | - Jiao Xiao
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China
| | - Fengqin Yang
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China
| | - Hongke Qu
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute and School of Basic Medicine Sciences, Central South University, Changsha, Hunan, China
| | - Chengxin Ye
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China
| | - Sile Chen
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China
| | - Yadong Guo
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China.
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161
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Yang P, Jiang J. In Reference to Evaluation of Oropharyngeal Cancer Information from Revolutionary Artificial Intelligence Chatbot. Laryngoscope 2024; 134:E18. [PMID: 38299720 DOI: 10.1002/lary.31315] [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: 12/02/2023] [Accepted: 12/06/2023] [Indexed: 02/02/2024]
Affiliation(s)
- Pingping Yang
- Department of Laboratory Medicine, People's Hospital of Qiannan Prefecture, Guizhou, China
| | - Jiuliang Jiang
- School of Clinical Medicine, Guizhou Medical University, Guizhou, China
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162
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Tao X, Zhao X, Liu H, Wang J, Tian C, Liu L, Ding Y, Chen X, Liu Y. Automatic Recognition of Concealed Fish Bones under Laryngoscopy: A Practical AI Model Based on YOLO-V5. Laryngoscope 2024; 134:2162-2169. [PMID: 37983879 DOI: 10.1002/lary.31175] [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: 08/04/2023] [Revised: 09/27/2023] [Accepted: 10/24/2023] [Indexed: 11/22/2023]
Abstract
BACKGROUND Fish bone impaction is one of the most common problems encountered in otolaryngology emergencies. Due to their small and transparent nature, as well as the complexity of pharyngeal anatomy, identifying fish bones efficiently under laryngoscopy requires substantial clinical experience. This study aims to create an AI model to assist clinicians in detecting pharyngeal fish bones more efficiently under laryngoscopy. METHODS Totally 3133 laryngoscopic images related to fish bones were collected for model training and validation. The images in the training dataset were trained using the YOLO-V5 algorithm model. After training, the model was validated and its performance was evaluated using a test dataset. The model's predictions were compared to those of human experts. Seven laryngoscopic videos related to fish bone were used to validate real-time target detection by the model. RESULTS The model trained in YOLO-V5 demonstrated good generalization and performance, with an average precision of 0.857 when the intersection over union (IOU) threshold was set to 0.5. The precision, recall rate, and F1 scores of the model are 0.909, 0.818, and 0.87, respectively. The overall accuracy of the model in the validation set was 0.821, comparable to that of ENT specialists. The model processed each image in 0.012 s, significantly faster than human processing (p < 0.001). Furthermore, the model exhibited outstanding performance in video recognition. CONCLUSION Our AI model based on YOLO-V5 effectively identifies and localizes fish bone foreign bodies in static laryngoscopic images and dynamic videos. It shows great potential for clinical application. LEVEL OF EVIDENCE 3 Laryngoscope, 134:2162-2169, 2024.
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Affiliation(s)
- Xiaoyao Tao
- Otorhinolaryngology Head and Neck Surgery Department, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xu Zhao
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Hairui Liu
- School of Information Engineering, China University of Geosciences, Beijing, China
| | - Jinqiao Wang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Chunhui Tian
- Otolaryngology-Head and Neck Surgery Department, Suzhou Hospital of Anhui Medical University, Suzhou, China
| | - Longsheng Liu
- Otolaryngology-Head and Neck Surgery Department, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Yujie Ding
- Otolaryngology-Head and Neck Surgery Department, Feixi County People's Hospital, Hefei, China
| | - Xue Chen
- Otolaryngology-Head and Neck Surgery Department, Feidong County People's Hospital, Hefei, China
| | - Yehai Liu
- Otorhinolaryngology Head and Neck Surgery Department, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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Mathis M, Steffner KR, Subramanian H, Gill GP, Girardi NI, Bansal S, Bartels K, Khanna AK, Huang J. Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology. J Cardiothorac Vasc Anesth 2024; 38:1211-1220. [PMID: 38453558 PMCID: PMC10999327 DOI: 10.1053/j.jvca.2024.02.004] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 01/30/2024] [Accepted: 02/05/2024] [Indexed: 03/09/2024]
Abstract
Artificial intelligence- (AI) and machine learning (ML)-based applications are becoming increasingly pervasive in the healthcare setting. This has in turn challenged clinicians, hospital administrators, and health policymakers to understand such technologies and develop frameworks for safe and sustained clinical implementation. Within cardiac anesthesiology, challenges and opportunities for AI/ML to support patient care are presented by the vast amounts of electronic health data, which are collected rapidly, interpreted, and acted upon within the periprocedural area. To address such challenges and opportunities, in this article, the authors review 3 recent applications relevant to cardiac anesthesiology, including depth of anesthesia monitoring, operating room resource optimization, and transthoracic/transesophageal echocardiography, as conceptual examples to explore strengths and limitations of AI/ML within healthcare, and characterize this evolving landscape. Through reviewing such applications, the authors introduce basic AI/ML concepts and methodologies, as well as practical considerations and ethical concerns for initiating and maintaining safe clinical implementation of AI/ML-based algorithms for cardiac anesthesia patient care.
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Affiliation(s)
- Michael Mathis
- Department of Anesthesiology, University of Michigan Medicine, Ann Arbor, MI
| | - Kirsten R Steffner
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA
| | - Harikesh Subramanian
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA
| | - George P Gill
- Department of Anesthesiology, Cedars Sinai, Los Angeles, CA
| | | | - Sagar Bansal
- Department of Anesthesiology and Perioperative Medicine, University of Missouri School of Medicine, Columbia, MO
| | - Karsten Bartels
- Department of Anesthesiology, University of Nebraska Medical Center, Omaha, NE
| | - Ashish K Khanna
- Department of Anesthesiology, Section on Critical Care Medicine, School of Medicine, Wake Forest University, Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, NC
| | - Jiapeng Huang
- Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY.
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164
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Delsoz M, Madadi Y, Raja H, Munir WM, Tamm B, Mehravaran S, Soleimani M, Djalilian A, Yousefi S. Performance of ChatGPT in Diagnosis of Corneal Eye Diseases. Cornea 2024; 43:664-670. [PMID: 38391243 DOI: 10.1097/ico.0000000000003492] [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: 09/26/2023] [Accepted: 12/28/2023] [Indexed: 02/24/2024]
Abstract
PURPOSE The aim of this study was to assess the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts. METHODS We randomly selected 20 cases of corneal diseases including corneal infections, dystrophies, and degenerations from a publicly accessible online database from the University of Iowa. We then input the text of each case description into ChatGPT-4.0 and ChatGPT-3.5 and asked for a provisional diagnosis. We finally evaluated the responses based on the correct diagnoses, compared them with the diagnoses made by 3 corneal specialists (human experts), and evaluated interobserver agreements. RESULTS The provisional diagnosis accuracy based on ChatGPT-4.0 was 85% (17 correct of 20 cases), whereas the accuracy of ChatGPT-3.5 was 60% (12 correct cases of 20). The accuracy of 3 corneal specialists compared with ChatGPT-4.0 and ChatGPT-3.5 was 100% (20 cases, P = 0.23, P = 0.0033), 90% (18 cases, P = 0.99, P = 0.6), and 90% (18 cases, P = 0.99, P = 0.6), respectively. The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 65% (13 cases), whereas the interobserver agreement between ChatGPT-4.0 and 3 corneal specialists was 85% (17 cases), 80% (16 cases), and 75% (15 cases), respectively. However, the interobserver agreement between ChatGPT-3.5 and each of 3 corneal specialists was 60% (12 cases). CONCLUSIONS The accuracy of ChatGPT-4.0 in diagnosing patients with various corneal conditions was markedly improved than ChatGPT-3.5 and promising for potential clinical integration. A balanced approach that combines artificial intelligence-generated insights with clinical expertise holds a key role for unveiling its full potential in eye care.
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Affiliation(s)
- Mohammad Delsoz
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, TN
| | - Yeganeh Madadi
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, TN
| | - Hina Raja
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, TN
| | - Wuqaas M Munir
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD
| | - Brendan Tamm
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD
| | - Shiva Mehravaran
- Department of Biology, School of Computer, Mathematical, and Natural Sciences, Morgan State University, Baltimore, MD
| | - Mohammad Soleimani
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran ; and
| | - Ali Djalilian
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL
| | - Siamak Yousefi
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, TN
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN
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165
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Sohail SS. A Promising Start and Not a Panacea: ChatGPT's Early Impact and Potential in Medical Science and Biomedical Engineering Research. Ann Biomed Eng 2024; 52:1131-1135. [PMID: 37540292 DOI: 10.1007/s10439-023-03335-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [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: 07/23/2023] [Accepted: 07/26/2023] [Indexed: 08/05/2023]
Abstract
The advent of artificial intelligence (AI) has catalyzed a revolutionary transformation across various industries, including healthcare. Medical applications of ChatGPT, a powerful language model based on the generative pre-trained transformer (GPT) architecture, encompass the creation of conversational agents capable of accessing and generating medical information from multiple sources and formats. This study investigates the research trends of large language models such as ChatGPT, GPT 4, and Google Bard, comparing their publication trends with early COVID-19 research. The findings underscore the current prominence of AI research and its potential implications in biomedical engineering. A search of the Scopus database on July 23, 2023, yielded 1,096 articles related to ChatGPT, with approximately 26% being medical science-related. Keywords related to artificial intelligence, natural language processing (NLP), LLM, and generative AI dominate ChatGPT research, while a focused representation of medical science research emerges, with emphasis on biomedical research and engineering. This analysis serves as a call to action for researchers, healthcare professionals, and policymakers to recognize and harness AI's potential in healthcare, particularly in the realm of biomedical research.
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Affiliation(s)
- Shahab Saquib Sohail
- Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, 110062, India.
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166
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Tamura Y, Nomura A, Kagiyama N, Mizuno A, Node K. Digitalomics, digital intervention, and designing future: The next frontier in cardiology. J Cardiol 2024; 83:318-322. [PMID: 38135148 DOI: 10.1016/j.jjcc.2023.12.002] [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: 09/02/2023] [Revised: 12/10/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023]
Abstract
The discipline of cardiology stands at a transformative juncture, primarily influenced by the surge in digital health technologies. These innovations hold the promise to redefine the realms of cardiovascular research and patient care, ushering in an era of individualized and data-driven treatments. This review delves into the heart of this evolution, introducing a comprehensive design for the future of cardiology. Emphasizing the emerging domains of "digitalomics" and "digital intervention", it explores how the integration of patient data, artificial intelligence-enabled diagnostics, and telehealth can lead to more streamlined and personalized cardiovascular health. The "digital-twin" model, a highlight of this approach, encapsulates individual patient characteristics, allowing for targeted treatments. The role of interdisciplinary collaboration in cardiovascular medicine is also underlined, emphasizing the importance of merging traditional cardiology with technological advancements. The convergence of traditional cardiology methods and digital health technologies, facilitated by a transdisciplinary approach, is set to chart a new course in cardiovascular health, emphasizing individualized care and improved clinical outcomes.
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Affiliation(s)
- Yuichi Tamura
- Pulmonary Hypertension Center, International University of Health and Welfare Mita Hospital, Tokyo, Japan; Department of Cardiology International University of Health and Welfare School of Medicine Narita, Japan; Cardiointelligence Inc., Tokyo, Japan.
| | - Akihiro Nomura
- College of Transdisciplinary Sciences for Innovation, Kanazawa University, Kanazawa, Japan; Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan; Frontier Institute of Tourism Sciences, Kanazawa University, Kanazawa, Japan; Department of Biomedical Informatics, CureApp Institute, Karuizawa, Japan
| | - Nobuyuki Kagiyama
- Department of Digital Health and Telemedicine R&D, Juntendo University, Tokyo, Japan; Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Atsushi Mizuno
- Department of Cardiovascular Medicine, St. Luke's International Hospital, Tokyo, Japan; Leonard Davis Institute for Health Economics, University of Pennsylvania, PA, USA
| | - Koichi Node
- Department of Cardiovascular Medicine, Saga University, Saga, Japan
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167
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Ross J, Hammouche S, Chen Y, Rockall AG. Beyond regulatory compliance: evaluating radiology artificial intelligence applications in deployment. Clin Radiol 2024; 79:338-345. [PMID: 38360516 DOI: 10.1016/j.crad.2024.01.026] [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: 08/25/2023] [Revised: 01/24/2024] [Accepted: 01/29/2024] [Indexed: 02/17/2024]
Abstract
The implementation of artificial intelligence (AI) applications in routine practice, following regulatory approval, is currently limited by practical concerns around reliability, accountability, trust, safety, and governance, in addition to factors such as cost-effectiveness and institutional information technology support. When a technology is new and relatively untested in a field, professional confidence is lacking and there is a sense of the need to go above the baseline level of validation and compliance. In this article, we propose an approach that goes beyond standard regulatory compliance for AI apps that are approved for marketing, including independent benchmarking in the lab as well as clinical audit in practice, with the aims of increasing trust and preventing harm.
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Affiliation(s)
- J Ross
- Department of Cancer and Surgery, Imperial College London, UK.
| | - S Hammouche
- Department of Cancer and Surgery, Imperial College London, UK
| | - Y Chen
- School of Medicine, University of Nottingham, UK
| | - A G Rockall
- Department of Cancer and Surgery, Imperial College London, UK
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168
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Haim GB, Braun A, Eden H, Burshtein L, Barash Y, Irony A, Klang E. AI in the ED: Assessing the efficacy of GPT models vs. physicians in medical score calculation. Am J Emerg Med 2024; 79:161-166. [PMID: 38447503 DOI: 10.1016/j.ajem.2024.02.016] [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: 09/26/2023] [Revised: 01/23/2024] [Accepted: 02/09/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND AND AIMS Artificial Intelligence (AI) models like GPT-3.5 and GPT-4 have shown promise across various domains but remain underexplored in healthcare. Emergency Departments (ED) rely on established scoring systems, such as NIHSS and HEART score, to guide clinical decision-making. This study aims to evaluate the proficiency of GPT-3.5 and GPT-4 against experienced ED physicians in calculating five commonly used medical scores. METHODS This retrospective study analyzed data from 150 patients who visited the ED over one week. Both AI models and two human physicians were tasked with calculating scores for NIH Stroke Scale, Canadian Syncope Risk Score, Alvarado Score for Acute Appendicitis, Canadian CT Head Rule, and HEART Score. Cohen's Kappa statistic and AUC values were used to assess inter-rater agreement and predictive performance, respectively. RESULTS The highest level of agreement was observed between the human physicians (Kappa = 0.681), while GPT-4 also showed moderate to substantial agreement with them (Kappa values of 0.473 and 0.576). GPT-3.5 had the lowest agreement with human scorers. These results highlight the superior predictive performance of human expertise over the currently available automated systems for this specific medical outcome. Human physicians achieved a higher ROC-AUC on 3 of the 5 scores, but none of the differences were statistically significant. CONCLUSIONS While AI models demonstrated some level of concordance with human expertise, they fell short in emulating the complex clinical judgments that physicians make. The study suggests that current AI models may serve as supplementary tools but are not ready to replace human expertise in high-stakes settings like the ED. Further research is needed to explore the capabilities and limitations of AI in emergency medicine.
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Affiliation(s)
- Gal Ben Haim
- Department of Emergency Medicine, Sheba Medical Center, Ramat-Gan, Israel; Tel Aviv University, Sackler Faculty of Medicine, Tel Aviv, Israel.
| | - Adi Braun
- Department of Emergency Medicine, Sheba Medical Center, Ramat-Gan, Israel; Tel Aviv University, Sackler Faculty of Medicine, Tel Aviv, Israel
| | - Haggai Eden
- Department of Emergency Medicine, Sheba Medical Center, Ramat-Gan, Israel; Tel Aviv University, Sackler Faculty of Medicine, Tel Aviv, Israel
| | - Livnat Burshtein
- Department of Emergency Medicine, Sheba Medical Center, Ramat-Gan, Israel
| | - Yiftach Barash
- Division of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel; DeepVision Lab, Sheba Medical Center, Tel Hashomer, Israel; Tel Aviv University, Sackler Faculty of Medicine, Tel Aviv, Israel
| | - Avinoah Irony
- Department of Emergency Medicine, Sheba Medical Center, Ramat-Gan, Israel; Tel Aviv University, Sackler Faculty of Medicine, Tel Aviv, Israel
| | - Eyal Klang
- Division of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel; DeepVision Lab, Sheba Medical Center, Tel Hashomer, Israel; Tel Aviv University, Sackler Faculty of Medicine, Tel Aviv, Israel
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169
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Li H, Hayward J, Aguilar LS, Franc JM. Desired clinical applications of artificial intelligence in emergency medicine: A Delphi study. Am J Emerg Med 2024; 79:217-220. [PMID: 38458952 DOI: 10.1016/j.ajem.2024.02.031] [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: 01/21/2024] [Revised: 02/01/2024] [Accepted: 02/08/2024] [Indexed: 03/10/2024] Open
Affiliation(s)
- Henry Li
- University of Alberta, Faculty of Medicine and Dentistry, Department of Emergency Medicine, 750 University Terrace Building, 8303-112 Street NW, Edmonton T6G 2T4, Canada.
| | - Jake Hayward
- University of Alberta, Faculty of Medicine and Dentistry, Department of Emergency Medicine, 750 University Terrace Building, 8303-112 Street NW, Edmonton T6G 2T4, Canada
| | - Leandro Solis Aguilar
- University of Alberta, Faculty of Medicine and Dentistry, Department of Biochemistry, 474 Medical Sciences Building, Edmonton T6G 2H7, Canada
| | - Jeffrey Michael Franc
- University of Alberta, Faculty of Medicine and Dentistry, Department of Emergency Medicine, 750 University Terrace Building, 8303-112 Street NW, Edmonton T6G 2T4, Canada; Università del Piemonte Orientale, Center for Research and Training in Disaster Medicine, Humanitarian Aid, and Global Health, Via Lanino 1, Novara 28100, Italy
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Jain E, Patel A, Parwani AV, Shafi S, Brar Z, Sharma S, Mohanty SK. Whole Slide Imaging Technology and Its Applications: Current and Emerging Perspectives. Int J Surg Pathol 2024; 32:433-448. [PMID: 37437093 DOI: 10.1177/10668969231185089] [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] [Indexed: 07/14/2023]
Abstract
Background. Whole slide imaging (WSI) represents a paradigm shift in pathology, serving as a necessary first step for a wide array of digital tools to enter the field. It utilizes virtual microscopy wherein glass slides are converted into digital slides and are viewed by pathologists by automated image analysis. Its impact on pathology workflow, reproducibility, dissemination of educational material, expansion of service to underprivileged areas, and institutional collaboration exemplifies a significant innovative movement. The recent US Food and Drug Administration approval to WSI for its use in primary surgical pathology diagnosis has opened opportunities for wider application of this technology in routine practice. Main Text. The ongoing technological advances in digital scanners, image visualization methods, and the integration of artificial intelligence-derived algorithms with these systems provide avenues to exploit its applications. Its benefits are innumerable such as ease of access through the internet, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides to name a few. Although the benefits of WSI to pathology practices are many, the complexities of implementation remain an obstacle to widespread adoption. Some barriers including the high cost, technical glitches, and most importantly professional hesitation to adopt a new technology have hindered its use in routine pathology. Conclusions. In this review, we summarize the technical aspects of WSI, its applications in diagnostic pathology, training, and research along with future perspectives. It also highlights improved understanding of the current challenges to implementation, as well as the benefits and successes of the technology. WSI provides a golden opportunity for pathologists to guide its evolution, standardization, and implementation to better acquaint them with the key aspects of this technology and its judicial use. Also, implementation of routine digital pathology is an extra step requiring resources which (currently) does not usually result increased efficiency or payment.
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Affiliation(s)
- Ekta Jain
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Ankush Patel
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Anil V Parwani
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Saba Shafi
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Zoya Brar
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Shivani Sharma
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Sambit K Mohanty
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
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171
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Sinha S. The Use of Uroflowmetry as a Diagnostic Test. Curr Urol Rep 2024; 25:99-107. [PMID: 38416321 DOI: 10.1007/s11934-024-01200-0] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/22/2024] [Indexed: 02/29/2024]
Abstract
PURPOSE OF REVIEW Uroflowmetry is widely used for initial non-invasive evaluation of lower urinary tract disorders. Current clinical use is mostly restricted to a scrutiny of the maximum flow rate and uroflow pattern recorded by a conventional flowmeter in a health care facility. There are several advancements in our understanding and in available technologies that promise to transform clinical utilization of this simple test. RECENT FINDINGS Several aspects of the uroflow test in addition to maximum flow rate and uroflow pattern show potential diagnostic utility. This includes flow acceleration, uroflow indices, uroflow-electromyography including lag time, stop uroflow test, and uroflow-based nomograms. There are initial attempts to use artificial intelligence in analysis. There is also new data with regard to factors influencing variability of uroflow testing that might influence the diagnostic value in as yet uncertain ways including diurnal variability, postural variability, locational variability, and operator variability. There are new technologies for uroflow testing in a home environment allowing for easy repetition. However, there are several challenges owing to a paucity of clinical data and standardization. There are also critical lacunae in terminology that need to be addressed. There are exciting new advancements in the field of uroflowmetry. However, there is need to standardize and validate the newer uroflow tracing analyses and technologies.
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Affiliation(s)
- Sanjay Sinha
- Department of Urology, Apollo Hospital, Hyderabad, India.
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172
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Rifino N, Bersano A, Padovani A, Conti GM, Cavallini A, Colombo L, Priori A, Pianese R, Gammone MR, Erbetta A, Ciceri EF, Sattin D, Varvello R, Parati EA, Scelzo E. Virtual hospital and artificial intelligence: a first step towards the application of an innovative health system for the care of rare cerebrovascular diseases. Neurol Sci 2024; 45:2087-2095. [PMID: 38017154 DOI: 10.1007/s10072-023-07206-9] [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: 10/18/2023] [Accepted: 11/14/2023] [Indexed: 11/30/2023]
Abstract
The development of virtual care options, including virtual hospital platforms, is rapidly changing the healthcare, mostly in the pandemic period, due to difficulties in in-person consultations. For this purpose, in 2020, a neurological Virtual Hospital (NOVHO) pilot study has been implemented, in order to experiment a multidisciplinary second opinion evaluation system for neurological diseases. Cerebrovascular diseases represent a preponderant part of neurological disorders. However, more than 30% of strokes remain of undetermined source, and rare CVD (rCVD) are often misdiagnosed. The lack of data on phenotype and clinical course of rCVD patients makes the diagnosis and the development of therapies challenging. Since the diagnosis and care of rCVDs require adequate expertise and instrumental tools, their management is mostly allocated to a few experienced hospitals, making difficult equity in access to care. Therefore, strategies for virtual consultations are increasingly applied with some advantage for patient management also in peripheral areas. Moreover, health data are becoming increasingly complex and require new technologies to be managed. The use of Artificial Intelligence is beginning to be applied to the healthcare system and together with the Internet of Things will enable the creation of virtual models with predictive abilities, bringing healthcare one step closer to personalized medicine. Herein, we will report on the preliminary results of the NOVHO project and present the methodology of a new project aimed at developing an innovative multidisciplinary and multicentre virtual care model, specific for rCVD (NOVHO-rCVD), which combines the virtual hospital approach and the deep-learning machine system.
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Affiliation(s)
- Nicola Rifino
- Cerebrovascular Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy.
| | - Anna Bersano
- Cerebrovascular Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Alessandro Padovani
- Department of Clinical and Experimental Sciences, Neurology Clinic, University of Brescia, Brescia, Italy
| | - Giancarlo Maria Conti
- Department of Neurology, ASST Nord Milano, Ospedale Bassini, Cinisello Balsamo, Italy
| | - Anna Cavallini
- Cerebrovascular Disease and Stroke Unit, IRCCS Fondazione Mondino, Pavia, Italy
| | | | - Alberto Priori
- Department of Neurology, Ospedale San Paolo, Milan, Italy
| | - Raffaella Pianese
- S.I.T.R.A, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | - Alessandra Erbetta
- Service of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Elisa Francesca Ciceri
- Diagnostic Radiology and Interventional Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Davide Sattin
- Istituti Clinici Scientifici Maugeri IRCCS Via Camaldoli 64, 20138, Milan, Italy
| | | | | | - Emma Scelzo
- Department of Neurology, Ospedale San Paolo, Milan, Italy
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173
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Schropp L, Sørensen APS, Devlin H, Matzen LH. Use of artificial intelligence software in dental education: A study on assisted proximal caries assessment in bitewing radiographs. Eur J Dent Educ 2024; 28:490-496. [PMID: 37961027 DOI: 10.1111/eje.12973] [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: 09/22/2022] [Revised: 02/14/2023] [Accepted: 10/13/2023] [Indexed: 11/15/2023]
Abstract
INTRODUCTION Teaching of dental caries diagnostics is an essential part of dental education. Diagnosing proximal caries is a challenging task, and automated systems applying artificial intelligence (AI) have been introduced to assist in this respect. Thus, the implementation of AI for teaching purposes may be considered. The aim of this study was to assess the impact of an AI software on students' ability to detect enamel-only proximal caries in bitewing radiographs (BWs) and to assess whether proximal tooth overlap interferes with caries detection. MATERIALS AND METHODS The study included 74 dental students randomly allocated to either a test or control group. At two sessions, both groups assessed proximal enamel caries in BWs. At the first session, the test group registered caries in 25 BWs using AI software (AssistDent®) and the control group without using AI. One month later, both groups detected caries in another 25 BWs in a clinical setup without using the software. The student's registrations were compared with a reference standard. Positive agreement (caries) and negative agreement (no caries) were calculated, and t-tests were applied to assess whether the test and control groups performed differently. Moreover, t-tests were applied to test whether proximal overlap interfered with caries registration. RESULTS At the first and second sessions, 56 and 52 tooth surfaces, respectively, were detected with enamel-only caries according to the reference standard. At session 1, no significant difference between the control (48%) and the test (42%) group was found for positive agreement (p = .08), whereas the negative agreement was higher for the test group (86% vs. 80%; p = .02). At session 2, there was no significant difference between the groups. The test group improved for positive agreement from session 1 to session 2 (p < .001), while the control group improved for negative agreement (p < .001). Thirty-eight per cent of the tooth surfaces overlapped, and the mean positive agreement and negative agreement were significantly lower for overlapping surfaces than non-overlapping surfaces (p < .001) in both groups. CONCLUSION Training with the AI software did not impact on dental students' ability to detect proximal enamel caries in bitewing radiographs although the positive agreement improved over time. It was revealed that proximal tooth overlap interfered with caries detection.
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Affiliation(s)
- Lars Schropp
- Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus C, Denmark
| | - Anders Peter Sejersdal Sørensen
- Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus C, Denmark
- Private practice, Tandlægerne Sydcentret, Kolding, Denmark
| | - Hugh Devlin
- Division of Dentistry, School of Medical Sciences, The University of Manchester, Manchester, UK
| | - Louise Hauge Matzen
- Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus C, Denmark
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Tejani A, Dowling T, Sanampudi S, Yazdani R, Canan A, Malja E, Xi Y, Abbara S, Peshock RM, Kay FU. Deep Learning for Detection of Pneumothorax and Pleural Effusion on Chest Radiographs: Validation Against Computed Tomography, Impact on Resident Reading Time, and Interreader Concordance. J Thorac Imaging 2024; 39:185-193. [PMID: 37884394 DOI: 10.1097/rti.0000000000000746] [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: 10/28/2023]
Abstract
PURPOSE To study the performance of artificial intelligence (AI) for detecting pleural pathology on chest radiographs (CXRs) using computed tomography as ground truth. PATIENTS AND METHODS Retrospective study of subjects undergoing CXR in various clinical settings. Computed tomography obtained within 24 hours of the CXR was used to volumetrically quantify pleural effusions (PEfs) and pneumothoraxes (Ptxs). CXR was evaluated by AI software (INSIGHT CXR; Lunit) and by 3 second-year radiology residents, followed by AI-assisted reassessment after a 3-month washout period. We used the area under the receiver operating characteristics curve (AUROC) to assess AI versus residents' performance and mixed-model analyses to investigate differences in reading time and interreader concordance. RESULTS There were 96 control subjects, 165 with PEf, and 101 with Ptx. AI-AUROC was noninferior to aggregate resident-AUROC for PEf (0.82 vs 0.86, P < 0.001) and Ptx (0.80 vs 0.84, P = 0.001) detection. AI-assisted resident-AUROC was higher but not significantly different from the baseline. AI-assisted reading time was reduced by 49% (157 vs 80 s per case, P = 0.009), and Fleiss kappa for Ptx detection increased from 0.70 to 0.78 ( P = 0.003). AI decreased detection error for PEf (odds ratio = 0.74, P = 0.024) and Ptx (odds ratio = 0.39, P < 0.001). CONCLUSION Current AI technology for the detection of PEf and Ptx on CXR was noninferior to second-year resident performance and could help decrease reading time and detection error.
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Affiliation(s)
- Ali Tejani
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX
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175
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Winkler JK, Kommoss KS, Toberer F, Enk A, Maul LV, Navarini AA, Hudson J, Salerni G, Rosenberger A, Haenssle HA. Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance. Eur J Cancer 2024; 202:114026. [PMID: 38547776 DOI: 10.1016/j.ejca.2024.114026] [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: 01/18/2024] [Revised: 03/11/2024] [Accepted: 03/14/2024] [Indexed: 04/21/2024]
Abstract
IMPORTANCE Total body photography for skin cancer screening is a well-established tool allowing documentation and follow-up of the entire skin surface. Artificial intelligence-based systems are increasingly applied for automated lesion detection and diagnosis. DESIGN AND PATIENTS In this prospective observational international multicentre study experienced dermatologists performed skin cancer screenings and identified clinically relevant melanocytic lesions (CRML, requiring biopsy or observation). Additionally, patients received 2D automated total body mapping (ATBM) with automated lesion detection (ATBM master, Fotofinder Systems GmbH). Primary endpoint was the percentage of CRML detected by the bodyscan software. Secondary endpoints included the percentage of correctly identified "new" and "changed" lesions during follow-up examinations. RESULTS At baseline, dermatologists identified 1075 CRML in 236 patients and 999 CRML (92.9%) were also detected by the automated software. During follow-up examinations dermatologists identified 334 CRMLs in 55 patients, with 323 (96.7%) also being detected by ATBM with automated lesions detection. Moreover, all new (n = 13) or changed CRML (n = 24) during follow-up were detected by the software. Average time requirements per baseline examination was 14.1 min (95% CI [12.8-15.5]). Subgroup analysis of undetected lesions revealed either technical (e.g. covering by clothing, hair) or lesion-specific reasons (e.g. hypopigmentation, palmoplantar sites). CONCLUSIONS ATBM with lesion detection software correctly detected the vast majority of CRML and new or changed CRML during follow-up examinations in a favourable amount of time. Our prospective international study underlines that automated lesion detection in TBP images is feasible, which is of relevance for developing AI-based skin cancer screenings.
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Affiliation(s)
- Julia K Winkler
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
| | | | - Ferdinand Toberer
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Alexander Enk
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Lara V Maul
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | | | - Jeremy Hudson
- North Queensland Skin Centre, Townsville, Queensland, Australia
| | - Gabriel Salerni
- Department of Dermatology, Hospital Provincial del Centenario de Rosario- Universidad Nacional de Rosario, Rosario, Argentina
| | - Albert Rosenberger
- Institute of Genetic Epidemiology, University Medical Center, Georg-August University of Goettingen, Goettingen, Germany
| | - Holger A Haenssle
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
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176
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Tejani AS, Ng YS, Xi Y, Rayan JC. Understanding and Mitigating Bias in Imaging Artificial Intelligence. Radiographics 2024; 44:e230067. [PMID: 38635456 DOI: 10.1148/rg.230067] [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: 04/20/2024]
Abstract
Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, bias in imaging AI is a complex topic that encompasses multiple coexisting definitions. Bias may refer to unequal preference to a person or group owing to preexisting attitudes or beliefs, either intentional or unintentional. However, cognitive bias refers to systematic deviation from objective judgment due to reliance on heuristics, and statistical bias refers to differences between true and expected values, commonly manifesting as systematic error in model prediction (ie, a model with output unrepresentative of real-world conditions). Clinical decisions informed by biased models may lead to patient harm due to action on inaccurate AI results or exacerbate health inequities due to differing performance among patient populations. However, while inequitable bias can harm patients in this context, a mindful approach leveraging equitable bias can address underrepresentation of minority groups or rare diseases. Radiologists should also be aware of bias after AI deployment such as automation bias, or a tendency to agree with automated decisions despite contrary evidence. Understanding common sources of imaging AI bias and the consequences of using biased models can guide preventive measures to mitigate its impact. Accordingly, the authors focus on sources of bias at stages along the imaging machine learning life cycle, attempting to simplify potentially intimidating technical terminology for general radiologists using AI tools in practice or collaborating with data scientists and engineers for AI tool development. The authors review definitions of bias in AI, describe common sources of bias, and present recommendations to guide quality control measures to mitigate the impact of bias in imaging AI. Understanding the terms featured in this article will enable a proactive approach to identifying and mitigating bias in imaging AI. Published under a CC BY 4.0 license. Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Rouzrokh and Erickson in this issue.
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Affiliation(s)
- Ali S Tejani
- From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390
| | - Yee Seng Ng
- From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390
| | - Yin Xi
- From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390
| | - Jesse C Rayan
- From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390
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Näher AF, Krumpal I, Antão EM, Ong E, Rojo M, Kaggwa F, Balzer F, Celi LA, Braune K, Wieler LH, Agha-Mir-Salim L. Measuring fairness preferences is important for artificial intelligence in health care. Lancet Digit Health 2024; 6:e302-e304. [PMID: 38670737 DOI: 10.1016/s2589-7500(24)00059-1] [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: 10/15/2023] [Revised: 02/15/2024] [Accepted: 03/08/2024] [Indexed: 04/28/2024]
Affiliation(s)
- Anatol-Fiete Näher
- Digital Global Public Health, Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam 14482, Germany; Institute of Medical Informatics, Charité-Universitätsmedizin Berlin, Berlin, Germany.
| | - Ivar Krumpal
- Faculty of Social Science and Philosophy, University of Leipzig, Leipzig, Germany
| | - Esther-Maria Antão
- Digital Global Public Health, Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam 14482, Germany
| | - Erika Ong
- College of Medicine, University of the Philippines, Manila, Philippines
| | - Marina Rojo
- Department of Public Health, School of Medicine, University of Buenos Aires, Buenos Aires, Argentina
| | - Fred Kaggwa
- Department of Computer Science, Faculty of Computing and Informatics, Mbarara University of Science and Technology, Mbarara, Uganda
| | - Felix Balzer
- Institute of Medical Informatics, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Katarina Braune
- Institute of Medical Informatics, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lothar H Wieler
- Digital Global Public Health, Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam 14482, Germany
| | - Louis Agha-Mir-Salim
- Institute of Medical Informatics, Charité-Universitätsmedizin Berlin, Berlin, Germany
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178
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Zhu L, Kong G, Liu C. Comments on: "Evaluation of the impact of large language learning models on articles submitted to Orthopaedics & Traumatology: Surgery & Research (OTSR): A significant increase in the use of artificial intelligence in 2023" by Maroteau G, An JS, Murgier J, et al. published in Orthop Traumatol Surg Res 2023;109(8):103720. Orthop Traumatol Surg Res 2024; 110:103859. [PMID: 38458315 DOI: 10.1016/j.otsr.2024.103859] [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: 01/24/2024] [Accepted: 02/28/2024] [Indexed: 03/10/2024]
Affiliation(s)
- Liming Zhu
- Department of Orthopaedic Surgery, the First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, China
| | - Guofei Kong
- Department of Orthopaedic Surgery, the First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, China
| | - Changhua Liu
- Department of Orthopaedic Surgery, the First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, China.
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179
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Kosar A, Asif M, Ahmad MB, Akram W, Mahmood K, Kumari S. Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey. Artif Intell Med 2024; 151:102858. [PMID: 38583369 DOI: 10.1016/j.artmed.2024.102858] [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: 04/22/2023] [Revised: 01/02/2024] [Accepted: 03/25/2024] [Indexed: 04/09/2024]
Abstract
The unpredictable pandemic came to light at the end of December 2019, known as the novel coronavirus, also termed COVID-19, identified by the World Health Organization (WHO). The virus first originated in Wuhan (China) and rapidly affected most of the world's population. This outbreak's impact is experienced worldwide because it causes high mortality risk, many cases, and economic falls. Around the globe, the total number of cases and deaths reported till November 12, 2022, were >600 million and 6.6 million, respectively. During the period of COVID-19, several diverse diagnostic techniques have been proposed. This work presents a systematic review of COVID-19 diagnostic techniques in response to such acts. Initially, these techniques are classified into different categories based on their working principle and detection modalities, i.e. chest X-ray imaging, cough sound or respiratory patterns, RT-PCR, antigen testing, and antibody testing. After that, a comparative analysis is performed to evaluate these techniques' efficacy which may help to determine an optimum solution for a particular scenario. The findings of the proposed work show that Artificial Intelligence plays a vital role in developing COVID-19 diagnostic techniques which support the healthcare system. The related work can be a footprint for all the researchers, available under a single umbrella. Additionally, all the techniques are long-lasting and can be used for future pandemics.
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Affiliation(s)
- Amna Kosar
- Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
| | - Muhammad Asif
- Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
| | - Maaz Bin Ahmad
- College of Computing and Information Sciences, Karachi Institute of Economics and Technology (KIET), Karachi, Pakistan
| | - Waseem Akram
- Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC
| | - Khalid Mahmood
- Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC.
| | - Saru Kumari
- Departement of Mathematics, Chaudhary Charan Singh University, Meerut, India
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180
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Chetwynd E. Ethical Use of Artificial Intelligence for Scientific Writing: Current Trends. J Hum Lact 2024; 40:211-215. [PMID: 38482810 PMCID: PMC11015711 DOI: 10.1177/08903344241235160] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 02/09/2024] [Indexed: 04/13/2024]
Affiliation(s)
- Ellen Chetwynd
- Department of Family Medicine, University of North Carolina, School of Medicine, Chapel Hill, NC, USA
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181
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Khan MA, Koh RGL, Rashidiani S, Liu T, Tucci V, Kumbhare D, Doyle TE. Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research. Artif Intell Med 2024; 151:102849. [PMID: 38574636 DOI: 10.1016/j.artmed.2024.102849] [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: 06/23/2023] [Revised: 03/15/2024] [Accepted: 03/19/2024] [Indexed: 04/06/2024]
Abstract
OBJECTIVE The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management. METHODS A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used. RESULTS After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP. CONCLUSION The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures. SIGNIFICANCE This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.
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Affiliation(s)
- Md Asif Khan
- Department of Electrical and Computer Engineering at McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Ryan G L Koh
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON M5G 2A2, Canada
| | - Sajjad Rashidiani
- Department of Electrical and Computer Engineering at McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Theodore Liu
- Department of Electrical and Computer Engineering at McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Victoria Tucci
- Faculty of Health Sciences at McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Dinesh Kumbhare
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON M5G 2A2, Canada
| | - Thomas E Doyle
- Department of Electrical and Computer Engineering at McMaster University, Hamilton, ON L8S 4K1, Canada.
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182
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Rokhshad R, Zhang P, Mohammad-Rahimi H, Pitchika V, Entezari N, Schwendicke F. Accuracy and consistency of chatbots versus clinicians for answering pediatric dentistry questions: A pilot study. J Dent 2024; 144:104938. [PMID: 38499280 DOI: 10.1016/j.jdent.2024.104938] [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: 11/11/2023] [Revised: 03/06/2024] [Accepted: 03/11/2024] [Indexed: 03/20/2024] Open
Abstract
OBJECTIVES Artificial Intelligence has applications such as Large Language Models (LLMs), which simulate human-like conversations. The potential of LLMs in healthcare is not fully evaluated. This pilot study assessed the accuracy and consistency of chatbots and clinicians in answering common questions in pediatric dentistry. METHODS Two expert pediatric dentists developed thirty true or false questions involving different aspects of pediatric dentistry. Publicly accessible chatbots (Google Bard, ChatGPT4, ChatGPT 3.5, Llama, Sage, Claude 2 100k, Claude-instant, Claude-instant-100k, and Google Palm) were employed to answer the questions (3 independent new conversations). Three groups of clinicians (general dentists, pediatric specialists, and students; n = 20/group) also answered. Responses were graded by two pediatric dentistry faculty members, along with a third independent pediatric dentist. Resulting accuracies (percentage of correct responses) were compared using analysis of variance (ANOVA), and post-hoc pairwise group comparisons were corrected using Tukey's HSD method. ACronbach's alpha was calculated to determine consistency. RESULTS Pediatric dentists were significantly more accurate (mean±SD 96.67 %± 4.3 %) than other clinicians and chatbots (p < 0.001). General dentists (88.0 % ± 6.1 %) also demonstrated significantly higher accuracy than chatbots (p < 0.001), followed by students (80.8 %±6.9 %). ChatGPT showed the highest accuracy (78 %±3 %) among chatbots. All chatbots except ChatGPT3.5 showed acceptable consistency (Cronbach alpha>0.7). CLINICAL SIGNIFICANCE Based on this pilot study, chatbots may be valuable adjuncts for educational purposes and for distributing information to patients. However, they are not yet ready to serve as substitutes for human clinicians in diagnostic decision-making. CONCLUSION In this pilot study, chatbots showed lower accuracy than dentists. Chatbots may not yet be recommended for clinical pediatric dentistry.
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Affiliation(s)
- Rata Rokhshad
- Department of Pediatric Dentistry, University of Alabama at Birmingham, Birmingham, AL, USA.
| | - Ping Zhang
- Department of Pediatric Dentistry, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Vinay Pitchika
- Department of Conservative Dentistry and Periodontology, LMU Klinikum Munich, Germany
| | - Niloufar Entezari
- Department of pediatric dentistry, School of Dentistry, Qom University of Medical Sciences, Qom, Iran
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Conservative Dentistry and Periodontology, LMU Klinikum Munich, Germany
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183
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Kim JB, Kim SJ, So M, Kim DK, Noh HR, Kim BJ, Choi YR, Kim D, Koo H, Kim T, Woo HG, Park SM. Artificial intelligence-driven drug repositioning uncovers efavirenz as a modulator of α-synuclein propagation: Implications in Parkinson's disease. Biomed Pharmacother 2024; 174:116442. [PMID: 38513596 DOI: 10.1016/j.biopha.2024.116442] [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: 12/21/2023] [Revised: 03/09/2024] [Accepted: 03/15/2024] [Indexed: 03/23/2024] Open
Abstract
Parkinson's disease (PD) is a complex neurodegenerative disorder with an unclear etiology. Despite significant research efforts, developing disease-modifying treatments for PD remains a major unmet medical need. Notably, drug repositioning is becoming an increasingly attractive direction in drug discovery, and computational approaches offer a relatively quick and resource-saving method for identifying testable hypotheses that promote drug repositioning. We used an artificial intelligence (AI)-based drug repositioning strategy to screen an extensive compound library and identify potential therapeutic agents for PD. Our AI-driven analysis revealed that efavirenz and nevirapine, approved for treating human immunodeficiency virus infection, had distinct profiles, suggesting their potential effects on PD pathophysiology. Among these, efavirenz attenuated α-synuclein (α-syn) propagation and associated neuroinflammation in the brain of preformed α-syn fibrils-injected A53T α-syn Tg mice and α-syn propagation and associated behavioral changes in the C. elegans BiFC model. Through in-depth molecular investigations, we found that efavirenz can modulate cholesterol metabolism and mitigate α-syn propagation, a key pathological feature implicated in PD progression by regulating CYP46A1. This study opens new avenues for further investigation into the mechanisms underlying PD pathology and the exploration of additional drug candidates using advanced computational methodologies.
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Affiliation(s)
- Jae-Bong Kim
- Department of Pharmacology, Ajou University School of Medicine, Suwon, Korea; Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea; Neuroscience Graduate Program, Department of Biomedical Sciences, Ajou University School of Medicine, Suwon, Korea
| | - Soo-Jeong Kim
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea
| | | | - Dong-Kyu Kim
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea
| | - Hye Rin Noh
- Department of Pharmacology, Ajou University School of Medicine, Suwon, Korea; Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea; Neuroscience Graduate Program, Department of Biomedical Sciences, Ajou University School of Medicine, Suwon, Korea
| | - Beom Jin Kim
- Department of Pharmacology, Ajou University School of Medicine, Suwon, Korea; Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea; Neuroscience Graduate Program, Department of Biomedical Sciences, Ajou University School of Medicine, Suwon, Korea
| | - Yu Ree Choi
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea
| | - Doyoon Kim
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea; Department of Physiology, Ajou University School of Medicine, Suwon, Korea
| | | | | | - Hyun Goo Woo
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea; Department of Physiology, Ajou University School of Medicine, Suwon, Korea
| | - Sang Myun Park
- Department of Pharmacology, Ajou University School of Medicine, Suwon, Korea; Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea; Neuroscience Graduate Program, Department of Biomedical Sciences, Ajou University School of Medicine, Suwon, Korea.
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184
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Rong Y, Chen Q, Fu Y, Yang X, Al-Hallaq HA, Wu QJ, Yuan L, Xiao Y, Cai B, Latifi K, Benedict SH, Buchsbaum JC, Qi XS. NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions. Int J Radiat Oncol Biol Phys 2024; 119:261-280. [PMID: 37972715 PMCID: PMC11023777 DOI: 10.1016/j.ijrobp.2023.10.033] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 09/16/2023] [Accepted: 10/14/2023] [Indexed: 11/19/2023]
Abstract
Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.
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Affiliation(s)
- Yi Rong
- Mayo Clinic Arizona, Phoenix, AZ
| | - Quan Chen
- City of Hope Comprehensive Cancer Center Duarte, CA
| | - Yabo Fu
- Memorial Sloan Kettering Cancer Center, Commack, NY
| | | | | | | | - Lulin Yuan
- Virginia Commonwealth University, Richmond, VA
| | - Ying Xiao
- University of Pennsylvania/Abramson Cancer Center, Philadelphia, PA
| | - Bin Cai
- The University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Stanley H Benedict
- University of California Davis Comprehensive Cancer Center, Sacramento, CA
| | | | - X Sharon Qi
- University of California Los Angeles, Los Angeles, CA
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185
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Lotter W, Hassett MJ, Schultz N, Kehl KL, Van Allen EM, Cerami E. Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions. Cancer Discov 2024; 14:711-726. [PMID: 38597966 DOI: 10.1158/2159-8290.cd-23-1199] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/29/2024] [Accepted: 02/28/2024] [Indexed: 04/11/2024]
Abstract
Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field. SIGNIFICANCE AI is increasingly being applied to all aspects of oncology, where several applications are maturing beyond research and development to direct clinical integration. This review summarizes the current state of the field through the lens of clinical translation along the clinical care continuum. Emerging areas are also highlighted, along with common challenges, evolving solutions, and potential future directions for the field.
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Affiliation(s)
- William Lotter
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Michael J Hassett
- Harvard Medical School, Boston, Massachusetts
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kenneth L Kehl
- Harvard Medical School, Boston, Massachusetts
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Eliezer M Van Allen
- Harvard Medical School, Boston, Massachusetts
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Ethan Cerami
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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186
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Kojima T, Fujimura S, Hasebe K, Okanoue Y, Shuya O, Yuki R, Shoji K, Hori R, Kishimoto Y, Omori K. Objective Assessment of Pathological Voice Using Artificial Intelligence Based on the GRBAS Scale. J Voice 2024; 38:561-566. [PMID: 34973892 DOI: 10.1016/j.jvoice.2021.11.021] [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: 09/02/2021] [Revised: 11/29/2021] [Accepted: 11/30/2021] [Indexed: 11/23/2022]
Abstract
OBJECTIVES The validity and reliability of the psychological assessment of auditory perceptions, as typified by the grade, roughness, breathiness, asthenia, and strain (GRBAS) scale, have been widely recognized. However, due to their subjective nature, inter- and intra-examiner reliability are unavoidable. In this study, we aimed to add objectivity to the GRBAS scale using artificial intelligence and to compare the accuracy of two methods-one based on Google's TensorFlow and another based on Apple's Core ML. METHODS The GRBAS scale of 1,377 vowel samples was evaluated and used as training data to create a machine learning model. We used TensorFlow and Apple's Create ML to create two machine learning models and examined the difference in their accuracies for classifying the severity of pathological Voice data based on the GRBAS scale. RESULTS Absolute comparisons are difficult to make because of the difference in methods; however, both training models could objectively evaluate GRBAS scales and were statistically correlated in G and B. CONCLUSION While TensorFlow requires creation of a training model from scratch, Create ML is a relatively easy way to create a training model for voice by adding training data for GRBAS scales to an existing training model for sounds. Although the data handling and learning methods are different, both models performed well. Findings from this study could be used for medical screening purposes, and there is the potential to change the clinical approach to voice diagnostics in the future.
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Affiliation(s)
- Tsuyoshi Kojima
- Department of Otolaryngology, Tenri Hospital, Tenri, Nara, Japan.
| | - Shintaro Fujimura
- Department of Otolaryngology-Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Koki Hasebe
- Department of Otolaryngology, Osaka Red Cross Hospital, Osaka, Japan
| | - Yusuke Okanoue
- Department of Otolaryngology, Tenri Hospital, Tenri, Nara, Japan
| | - Otsuki Shuya
- Department of Otolaryngology, Tenri Hospital, Tenri, Nara, Japan
| | - Ryohei Yuki
- Department of Otolaryngology, Tenri Hospital, Tenri, Nara, Japan
| | - Kazuhiko Shoji
- Department of Otolaryngology, Tenri Hospital, Tenri, Nara, Japan
| | - Ryusuke Hori
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Fujita Health University, Aichi, Japan
| | - Yo Kishimoto
- Department of Otolaryngology-Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Koichi Omori
- Department of Otolaryngology-Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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187
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Oeding JF, Kunze KN, Messer CJ, Pareek A, Fufa DT, Pulos N, Rhee PC. Diagnostic Performance of Artificial Intelligence for Detection of Scaphoid and Distal Radius Fractures: A Systematic Review. J Hand Surg Am 2024; 49:411-422. [PMID: 38551529 DOI: 10.1016/j.jhsa.2024.01.020] [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: 08/23/2023] [Revised: 01/19/2024] [Accepted: 01/31/2024] [Indexed: 05/05/2024]
Abstract
PURPOSE To review the existing literature to (1) determine the diagnostic efficacy of artificial intelligence (AI) models for detecting scaphoid and distal radius fractures and (2) compare the efficacy to human clinical experts. METHODS PubMed, OVID/Medline, and Cochrane libraries were queried for studies investigating the development, validation, and analysis of AI for the detection of scaphoid or distal radius fractures. Data regarding study design, AI model development and architecture, prediction accuracy/area under the receiver operator characteristic curve (AUROC), and imaging modalities were recorded. RESULTS A total of 21 studies were identified, of which 12 (57.1%) used AI to detect fractures of the distal radius, and nine (42.9%) used AI to detect fractures of the scaphoid. AI models demonstrated good diagnostic performance on average, with AUROC values ranging from 0.77 to 0.96 for scaphoid fractures and from 0.90 to 0.99 for distal radius fractures. Accuracy of AI models ranged between 72.0% to 90.3% and 89.0% to 98.0% for scaphoid and distal radius fractures, respectively. When compared to clinical experts, 13 of 14 (92.9%) studies reported that AI models demonstrated comparable or better performance. The type of fracture influenced model performance, with worse overall performance on occult scaphoid fractures; however, models trained specifically on occult fractures demonstrated substantially improved performance when compared to humans. CONCLUSIONS AI models demonstrated excellent performance for detecting scaphoid and distal radius fractures, with the majority demonstrating comparable or better performance compared with human experts. Worse performance was demonstrated on occult fractures. However, when trained specifically on difficult fracture patterns, AI models demonstrated improved performance. CLINICAL RELEVANCE AI models can help detect commonly missed occult fractures while enhancing workflow efficiency for distal radius and scaphoid fracture diagnoses. As performance varies based on fracture type, future studies focused on wrist fracture detection should clearly define whether the goal is to (1) identify difficult-to-detect fractures or (2) improve workflow efficiency by assisting in routine tasks.
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Affiliation(s)
- Jacob F Oeding
- School of Medicine, Mayo Clinic Alix School of Medicine, Rochester, MN; Department of Orthopaedics, Institute of Clinical Sciences, The Sahlgrenska Academy, University of Gotenburg, Gothenburg, Sweden.
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Caden J Messer
- School of Medicine, Mayo Clinic Alix School of Medicine, Rochester, MN
| | - Ayoosh Pareek
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Duretti T Fufa
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Nicholas Pulos
- Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN
| | - Peter C Rhee
- Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN
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188
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Zhang X, Lu C, Tian J, Zeng L, Wang Y, Sun W, Han H, Kang J. Artificial intelligence optimization and controllable slow-release iron sulfide realizes efficient separation of copper and arsenic in strongly acidic wastewater. J Environ Sci (China) 2024; 139:293-307. [PMID: 38105056 DOI: 10.1016/j.jes.2023.05.038] [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: 04/18/2023] [Revised: 05/16/2023] [Accepted: 05/28/2023] [Indexed: 12/19/2023]
Abstract
Iron sulfide (FeS) is a promising material for separating copper and arsenic from strongly acidic wastewater due to its S2- slow-release effect. However, uncertainties arise because of the constant changes in wastewater composition, affecting the selection of operating parameters and FeS types. In this study, the aging method was first used to prepare various controllable FeS nanoparticles to weaken the arsenic removal ability without affecting the copper removal. Orthogonal experiments were conducted, and the results identified the Cu/As ratio, H2SO4 concentration, and FeS dosage as the three main factors influencing the separation efficiency. The backpropagation artificial neural network (BP-ANN) model was established to determine the relationship between the influencing factors and the separation efficiency. The correlation coefficient (R) of overall model was 0.9923 after optimizing using genetic algorithm (GA). The BP-GA model was also solved using GA under specific constraints, predicting the best solution for the separation process in real-time. The predicted results show that the high temperature and long aging time of FeS were necessary to gain high separation efficiency, and the maximum separation factor can reached 1,400. This study provides a suitable sulfurizing material and a set of methods and models with robust flexibility that can successfully predict the separation efficiency of copper and arsenic from highly acidic environments.
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Affiliation(s)
- Xingfei Zhang
- School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China
| | - Chenglong Lu
- Centre for Mined Land Rehabilitation, Sustainable Minerals Institute, The University of Queensland, Brisbane 4072, Australia
| | - Jia Tian
- School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China
| | - Liqiang Zeng
- School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China
| | - Yufeng Wang
- School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China
| | - Wei Sun
- School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China
| | - Haisheng Han
- School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China.
| | - Jianhua Kang
- School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China
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189
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Kokot NC, Davis RJ. In Response to Evaluation of Oropharyngeal Cancer Information from Revolutionary Artificial Intelligence Chatbot. Laryngoscope 2024; 134:E19. [PMID: 38299696 DOI: 10.1002/lary.31313] [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: 12/30/2023] [Accepted: 01/05/2024] [Indexed: 02/02/2024]
Affiliation(s)
- Niels C Kokot
- Caruso Department of Otolaryngology-Head & Neck Surgery, Keck School of Medicine of the University of Southern California, Los Angeles, California, U.S.A
| | - Ryan J Davis
- Keck School of Medicine of the University of Southern California, Los Angeles, California, U.S.A
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190
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Kumar N, Acharya V. Advances in machine intelligence-driven virtual screening approaches for big-data. Med Res Rev 2024; 44:939-974. [PMID: 38129992 DOI: 10.1002/med.21995] [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: 09/12/2022] [Revised: 07/15/2023] [Accepted: 10/29/2023] [Indexed: 12/23/2023]
Abstract
Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.
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Affiliation(s)
- Neeraj Kumar
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
| | - Vishal Acharya
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
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191
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Lee E, Amadi C, Williams MC, Agarwal PP. Coronary Artery Disease: Role of Computed Tomography and Recent Advances. Radiol Clin North Am 2024; 62:385-398. [PMID: 38553176 DOI: 10.1016/j.rcl.2023.12.017] [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] [Indexed: 04/02/2024]
Abstract
In this review, the authors summarize the role of coronary computed tomography angiography and coronary artery calcium scoring in different clinical presentations of chest pain and preventative care and discuss future directions and new technologies such as pericoronary fat inflammation and the growing footprint of artificial intelligence in cardiovascular medicine.
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Affiliation(s)
- Elizabeth Lee
- Department of Radiology, Michigan Medicine, 1500 East Medical Center Drive, TC B1-148, Ann Arbor, MI 48109-5030, USA.
| | - Chiemezie Amadi
- Department of Radiology, Michigan Medicine, 1500 Medical Center Drive, Room 5481, Ann Arbor, MI 48109-5868, USA
| | - Michelle C Williams
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, The Queen's Medical Research Institute, Edinburg BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Prachi P Agarwal
- Department of Radiology, Division of Cardiothoracic Radiology, Michigan Medicine, 1500 East Medical Center Drive SPC 5868, Ann Arbor, MI 48109, USA
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192
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Kirchner J, Gesch J, Gercek M, Piran M, Friedrichs K, Pfister R, Rudolph F, Potratz M, Goncharov A, Ivannikova M, Rudolph V, Rudolph TK. Analysis of tricuspid annulus dimensions and RCA-proximity with artificial intelligence-based software for procedural planning of percutaneous tricuspid annuloplasty. J Cardiovasc Comput Tomogr 2024; 18:309-310. [PMID: 38290934 DOI: 10.1016/j.jcct.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: 10/13/2023] [Revised: 12/15/2023] [Accepted: 01/06/2024] [Indexed: 02/01/2024]
Affiliation(s)
- Johannes Kirchner
- Clinic for General and Interventional Cardiology/Angiology, Herz- und Diabeteszentrum NRW, Ruhr-Universität Bochum, Bad Oeynhausen, Germany.
| | - Johannes Gesch
- Department of Cardiology, Heart Center, University of Cologne, Cologne, Germany
| | - Muhammed Gercek
- Clinic for General and Interventional Cardiology/Angiology, Herz- und Diabeteszentrum NRW, Ruhr-Universität Bochum, Bad Oeynhausen, Germany
| | - Misagh Piran
- Department of Radiology, Nuclear Medicine and Molecular Imaging, Herz- und Diabeteszentrum NRW, Ruhr-Universität Bochum, Bad Oeynhausen, Germany
| | - Kai Friedrichs
- Clinic for General and Interventional Cardiology/Angiology, Herz- und Diabeteszentrum NRW, Ruhr-Universität Bochum, Bad Oeynhausen, Germany
| | - Roman Pfister
- Department of Cardiology, Heart Center, University of Cologne, Cologne, Germany
| | - Felix Rudolph
- Clinic for General and Interventional Cardiology/Angiology, Herz- und Diabeteszentrum NRW, Ruhr-Universität Bochum, Bad Oeynhausen, Germany
| | - Max Potratz
- Clinic for General and Interventional Cardiology/Angiology, Herz- und Diabeteszentrum NRW, Ruhr-Universität Bochum, Bad Oeynhausen, Germany
| | - Arsenyi Goncharov
- Clinic for General and Interventional Cardiology/Angiology, Herz- und Diabeteszentrum NRW, Ruhr-Universität Bochum, Bad Oeynhausen, Germany
| | - Maria Ivannikova
- Clinic for General and Interventional Cardiology/Angiology, Herz- und Diabeteszentrum NRW, Ruhr-Universität Bochum, Bad Oeynhausen, Germany
| | - Volker Rudolph
- Clinic for General and Interventional Cardiology/Angiology, Herz- und Diabeteszentrum NRW, Ruhr-Universität Bochum, Bad Oeynhausen, Germany
| | - Tanja K Rudolph
- Clinic for General and Interventional Cardiology/Angiology, Herz- und Diabeteszentrum NRW, Ruhr-Universität Bochum, Bad Oeynhausen, Germany
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193
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Tanabe H, Shiraishi T, Sato H, Nihei M, Inoue T, Kuwabara C. A concept for emotion recognition systems for children with profound intellectual and multiple disabilities based on artificial intelligence using physiological and motion signals. Disabil Rehabil Assist Technol 2024; 19:1319-1326. [PMID: 36695503 DOI: 10.1080/17483107.2023.2170478] [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: 02/24/2022] [Revised: 11/28/2022] [Accepted: 01/16/2023] [Indexed: 01/26/2023]
Abstract
PURPOSE This study proposes a concept for emotion recognition systems for children with profound intellectual and multiple disabilities (PIMD) based on artificial intelligence (AI) using physiological and motion signals. METHODS First, the heartbeat interval (R-R interval, RRI) of a child with PIMD was measured, and the correlation between the RRI and emotion was briefly tested in a preliminary experiment. Then, a concept based on AI for emotion recognition systems for children with PIMD was created using physiological and motion signals, and an emotion recognition system based on the proposed concept was developed using a random forest classifier taking as inputs the RRI, eye gaze, and other data acquired using low physical burden sensors. Subsequently, the developed emotion recognition system was evaluated, validating the proposed concept. Finally, we proposed a validated concept for emotion recognition systems. RESULTS A correlation was found between the RRI and emotion. The emotion recognition system was created based on the proposed concept and tested. According to the results, the recognition rate of "negative" and "not negative" of 70.4% ± 6.1% (Mean ± S.D.) of the developed emotion recognition system was higher than 48.5% ± 5.0% of an unfamiliar person used as a control. CONCLUSION The results indicate that the proposed concept for emotion recognition systems is useful for communicating with children with PIMD.
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Affiliation(s)
- Hiroki Tanabe
- Graduate School of Environment and Information Sciences, Yokohama National University, Yokohama, Japan
| | - Toshihiko Shiraishi
- Graduate School of Environment and Information Sciences, Yokohama National University, Yokohama, Japan
| | - Haruhiko Sato
- Department of Rehabilitation, Kansai Medical University, Hirakata, Japan
| | - Misato Nihei
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Takenobu Inoue
- Department of Assistive Technology, Research Institute of National Rehabilitation Center for Persons with Disabilities, Tokorozawa, Japan
| | - Chika Kuwabara
- Center for Developmental Disabilities of Yokosuka, Yokosuka, Japan
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Agarwal A, Stoff B. Ethics of using generative pretrained transformer and artificial intelligence systems for patient prior authorizations. J Am Acad Dermatol 2024; 90:1121-1122. [PMID: 37088200 DOI: 10.1016/j.jaad.2023.04.030] [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: 03/01/2023] [Revised: 04/13/2023] [Accepted: 04/16/2023] [Indexed: 04/25/2023]
Affiliation(s)
- Aneesh Agarwal
- Department of Medical Education, Icahn School of Medicine at Mount Sinai, New York, New York.
| | - Benjamin Stoff
- Department of Dermatology, Emory School of Medicine, Atlanta, Georgia; Emory Center for Ethics, Atlanta, Georgia
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195
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He H, Wang L, Wang X, Zhang M. Artificial intelligence in serum protein electrophoresis: history, state of the art, and perspective. Crit Rev Clin Lab Sci 2024; 61:226-240. [PMID: 37909425 DOI: 10.1080/10408363.2023.2274325] [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: 04/02/2023] [Accepted: 10/19/2023] [Indexed: 11/03/2023]
Abstract
Serum protein electrophoresis (SPEP) is a valuable laboratory test that separates proteins from the blood based on their electrical charge and size. The test can detect and analyze various protein abnormalities, and the interpretation of graphic SPEP features plays a crucial role in the diagnosis and monitoring of conditions, such as myeloma. Furthermore, the advancement of artificial intelligence (AI) technology presents an opportunity to enhance the organization and optimization of analytical procedures by streamlining the process and reducing the potential for human error in SPEP analysis, thereby making the process more efficient and reliable. For instance, AI can assist in the identification of protein peaks, the calculation of their relative proportions, and the detection of abnormalities or inconsistencies. This review explores the characteristics and limitations of AI in SPEP, and the role of standardization in improving its clinical utility. It also offers guidance on the rational ordering and interpreting of SPEP results in conjunction with AI. Such integration can effectively reduce the time and resources required for manual analysis while improving the accuracy and consistency of the results.
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Affiliation(s)
- He He
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Lingfeng Wang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Xia Wang
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Mei Zhang
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
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196
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Esmaeilzadeh P. Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artif Intell Med 2024; 151:102861. [PMID: 38555850 DOI: 10.1016/j.artmed.2024.102861] [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] [Revised: 03/19/2024] [Accepted: 03/25/2024] [Indexed: 04/02/2024]
Abstract
Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational efficiencies, or improved medical service accessibility. However, deploying AI-driven tools in the healthcare ecosystem could be challenging. This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale. As AI continues to make strides in healthcare, its integration presents various challenges, including production timelines, trust generation, privacy concerns, algorithmic biases, and data scarcity. The paper highlights that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools. Healthcare organizations should re-evaluate root problems such as misaligned financial incentives (e.g., fee-for-service models), dysfunctional medical workflows (e.g., high rates of patient readmissions), poor care coordination between different providers, fragmented electronic health records systems, and inadequate patient education and engagement models in tandem with AI adoption. This study also explores the need for a cultural shift in viewing AI not as a threat but as an enabler that can enhance healthcare delivery and create new employment opportunities while emphasizing the importance of addressing underlying operational issues. The necessity of investments beyond finance is discussed, emphasizing the importance of human capital, continuous learning, and a supportive environment for AI integration. The paper also highlights the crucial role of clear regulations in building trust, ensuring safety, and guiding the ethical use of AI, calling for coherent frameworks addressing transparency, model accuracy, data quality control, liability, and ethics. Furthermore, this paper underscores the importance of advancing AI literacy within academia to prepare future healthcare professionals for an AI-driven landscape. Through careful navigation and proactive measures addressing these challenges, the healthcare community can harness AI's transformative power responsibly and effectively, revolutionizing healthcare delivery and patient care. The paper concludes with a vision and strategic suggestions for the future of healthcare with AI, emphasizing thoughtful, responsible, and innovative engagement as the pathway to realizing its full potential to unlock immense benefits for healthcare organizations, physicians, nurses, and patients while proactively mitigating risks.
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Affiliation(s)
- Pouyan Esmaeilzadeh
- Department of Information Systems and Business Analytics, College of Business, Florida International University (FIU), Modesto A. Maidique Campus, 11200 S.W. 8th St, RB 261B, Miami, FL 33199, United States.
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197
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Spoladore D, Tosi M, Lorenzini EC. Ontology-based decision support systems for diabetes nutrition therapy: A systematic literature review. Artif Intell Med 2024; 151:102859. [PMID: 38564880 DOI: 10.1016/j.artmed.2024.102859] [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: 09/12/2023] [Revised: 02/05/2024] [Accepted: 03/25/2024] [Indexed: 04/04/2024]
Abstract
Diabetes is a non-communicable disease that has reached epidemic proportions, affecting 537 million people globally. Artificial Intelligence can support patients or clinicians in diabetes nutrition therapy - the first medical therapy in most cases of Type 1 and Type 2 diabetes. In particular, ontology-based recommender and decision support systems can deliver a computable representation of experts' knowledge, thus delivering patient-tailored nutritional recommendations or supporting clinical personnel in identifying the most suitable diet. This work proposes a systematic literature review of the domain ontologies describing diabetes in such systems, identifying their underlying conceptualizations, the users targeted by the systems, the type(s) of diabetes tackled, and the nutritional recommendations provided. This review also delves into the structure of the domain ontologies, highlighting several aspects that may hinder (or foster) their adoption in recommender and decision support systems for diabetes nutrition therapy. The results of this review process allow to underline how recommendations are formulated and the role of clinical experts in developing domain ontologies, outlining the research trends characterizing this research area. The results also allow for identifying research directions that can foster a preeminent role for clinical experts and clinical guidelines in a cooperative effort to make ontologies more interoperable - thus enabling them to play a significant role in the decision-making processes about diabetes nutrition therapy.
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Affiliation(s)
- Daniele Spoladore
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing - National Research Council, (CNR-STIIMA), Lecco, Italy.
| | - Martina Tosi
- Department of Health Sciences, University of Milan, 20142 Milan, Italy; Institute of Agricultural Biology and Biotechnology - National Research Council (CNR-IBBA), Milan, Italy.
| | - Erna Cecilia Lorenzini
- Department of Biomedical Sciences for Health, University of Milan, I-20133 Milan, Italy.
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198
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Brozović J, Mikulić B, Tomas M, Juzbašić M, Blašković M. Assessing the performance of Bing Chat artificial intelligence: Dental exams, clinical guidelines, and patients' frequent questions. J Dent 2024; 144:104927. [PMID: 38458379 DOI: 10.1016/j.jdent.2024.104927] [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: 03/03/2024] [Accepted: 03/05/2024] [Indexed: 03/10/2024] Open
Abstract
OBJECTIVES Bing Chat is a large language model artificial intelligence (AI) with online search and text generating capabilities. This study assessed its performance within the scope of dentistry in: (a) tackling exam questions for dental students, (ii) providing guidelines for dental practitioners, and (iii) answering patients' frequently asked questions. We discuss the potential of clinical tutoring, common patient communication and impact on academia. METHODS With the aim of assessing AI's performance in dental exams, Bing Chat was presented with 532 multiple-choice questions and awarded scores based on its answers. In evaluating guidelines for clinicians, a further set of 15 questions, each with 2 follow-up questions on clinical protocols, was presented to the AI. The answers were assessed by 4 reviewers using electronic visual analog scale. In evaluating answers to patients' frequently asked questions, another list of 15 common questions was included in the session, with respective outputs assessed. RESULTS Bing Chat correctly answered 383 out of 532 multiple-choice questions in dental exam part, achieving a score of 71.99 %. As for outlining clinical protocols for practitioners, the overall assessment score was 81.05 %. In answering patients' frequently asked questions, Bing Chat achieved an overall mean score of 83.8 %. The assessments demonstrated low inter-rater reliability. CONCLUSIONS The overall performance of Bing Chat was above the regularly adopted passing scores, particularly in answering patient's frequently asked questions. The generated content may have biased sources. These results suggest the importance of raising clinicians' awareness of AI's benefits and risks, as well as timely adaptations of dental education curricula, and safeguarding its use in dentistry and healthcare in general. CLINICAL SIGNIFICANCE Bing Chat AI performed above the passing threshold in three categories, and thus demonstrated potential for educational assistance, clinical tutoring, and answering patients' questions. We recommend popularizing its benefits and risks among students and clinicians, while maintaining awareness of possible false information.
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Affiliation(s)
- Juraj Brozović
- Assistant Professor, Ph.D., DMD, Specialist in Oral Surgery, Faculty of Dental Medicine and Health, University of Osijek, Croatia.
| | - Barbara Mikulić
- Assistant, DMD, Faculty of Dental Medicine and Health, University of Osijek, Croatia
| | - Matej Tomas
- Assistant, Ph.D., DMD, Faculty of Dental Medicine and Health, University of Osijek, Croatia
| | - Martina Juzbašić
- Assistant, DMD, Faculty of Dental Medicine and Health, University of Osijek, Croatia
| | - Marko Blašković
- Assistant, DMD, Specialist in Oral Surgery, Department of Oral Surgery, Faculty of Dental Medicine, University of Rijeka, Croatia
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Hurkmans C, Bibault JE, Clementel E, Dhont J, van Elmpt W, Kantidakis G, Andratschke N. Assessment of bias in scoring of AI-based radiotherapy segmentation and planning studies using modified TRIPOD and PROBAST guidelines as an example. Radiother Oncol 2024; 194:110196. [PMID: 38432311 DOI: 10.1016/j.radonc.2024.110196] [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: 11/27/2023] [Revised: 01/29/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
BACKGROUND AND PURPOSE Studies investigating the application of Artificial Intelligence (AI) in the field of radiotherapy exhibit substantial variations in terms of quality. The goal of this study was to assess the amount of transparency and bias in scoring articles with a specific focus on AI based segmentation and treatment planning, using modified PROBAST and TRIPOD checklists, in order to provide recommendations for future guideline developers and reviewers. MATERIALS AND METHODS The TRIPOD and PROBAST checklist items were discussed and modified using a Delphi process. After consensus was reached, 2 groups of 3 co-authors scored 2 articles to evaluate usability and further optimize the adapted checklists. Finally, 10 articles were scored by all co-authors. Fleiss' kappa was calculated to assess the reliability of agreement between observers. RESULTS Three of the 37 TRIPOD items and 5 of the 32 PROBAST items were deemed irrelevant. General terminology in the items (e.g., multivariable prediction model, predictors) was modified to align with AI-specific terms. After the first scoring round, further improvements of the items were formulated, e.g., by preventing the use of sub-questions or subjective words and adding clarifications on how to score an item. Using the final consensus list to score the 10 articles, only 2 out of the 61 items resulted in a statistically significant kappa of 0.4 or more demonstrating substantial agreement. For 41 items no statistically significant kappa was obtained indicating that the level of agreement among multiple observers is due to chance alone. CONCLUSION Our study showed low reliability scores with the adapted TRIPOD and PROBAST checklists. Although such checklists have shown great value during development and reporting, this raises concerns about the applicability of such checklists to objectively score scientific articles for AI applications. When developing or revising guidelines, it is essential to consider their applicability to score articles without introducing bias.
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Affiliation(s)
- Coen Hurkmans
- Dept. of Radiation Oncology, Catharina Hospital Eindhoven, the Netherlands; Dept. of Electrical Engineering, Technical University Eindhoven, the Netherlands.
| | - Jean-Emmanuel Bibault
- Dept. of Radiation Oncology, Hôpital Européen Georges Pompidou, Université Paris Cité, Paris, France
| | - Enrico Clementel
- European Organisation for the Research and Treatment of Cancer (EORTC), Brussels, Belgium
| | - Jennifer Dhont
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B), Institut Jules Bordet, Department of Medical Physics, Brussels, Belgium; Université Libre De Bruxelles (ULB), Radiophysics and MRI Physics Laboratory, Brussels, Belgium
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Georgios Kantidakis
- European Organisation for the Research and Treatment of Cancer (EORTC), Brussels, Belgium
| | - Nicolaus Andratschke
- Dept. of Radiation Oncology, University Hospital of Zurich, The University of Zurich, Zurich, Switzerland
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200
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Wu Z, Yu X, Wang F, Xu C. Application of artificial intelligence in dental implant prognosis: A scoping review. J Dent 2024; 144:104924. [PMID: 38467177 DOI: 10.1016/j.jdent.2024.104924] [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: 02/19/2024] [Accepted: 03/03/2024] [Indexed: 03/13/2024] Open
Abstract
OBJECTIVES The purpose of this scoping review was to evaluate the performance of artificial intelligence (AI) in the prognosis of dental implants. DATA Studies that analyzed the performance of AI models in the prediction of implant prognosis based on medical records or radiographic images. Quality assessment was conducted using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies. SOURCES This scoping review included studies published in English up to October 2023 in MEDLINE/PubMed, Embase, Cochrane Library, and Scopus. A manual search was also performed. STUDY SELECTION Of 892 studies, full-text analysis was conducted in 36 studies. Twelve studies met the inclusion criteria. Eight used deep learning models, 3 applied traditional machine learning algorithms, and 1 study combined both types. The performance was quantified using accuracy, sensitivity, specificity, precision, F1 score, and receiver operating characteristic area under curves (ROC AUC). The prognostic accuracy was analyzed and ranged from 70 % to 96.13 %. CONCLUSIONS AI is a promising tool in evaluating implant prognosis, but further enhancements are required. Additional radiographic and clinical data are needed to improve AI performance in implant prognosis. CLINICAL SIGNIFICANCE AI can predict the prognosis of dental implants based on radiographic images or medical records. As a result, clinicians can receive predicted implant prognosis with the assistance of AI before implant placement and make informed decisions.
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Affiliation(s)
- Ziang Wu
- Department of Prosthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; College of Stomatology, Shanghai Jiao Tong University, Shanghai, China; National Center for Stomatology, Shanghai, China; National Clinical Research Center for Oral Diseases, Shanghai, China; Shanghai Key Laboratory of Stomatology, Shanghai, China; Shanghai Research Institute of Stomatology, Shanghai, China
| | - Xinbo Yu
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China; National Center for Stomatology, Shanghai, China; National Clinical Research Center for Oral Diseases, Shanghai, China; Shanghai Key Laboratory of Stomatology, Shanghai, China; Shanghai Research Institute of Stomatology, Shanghai, China; Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Feng Wang
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China; National Center for Stomatology, Shanghai, China; National Clinical Research Center for Oral Diseases, Shanghai, China; Shanghai Key Laboratory of Stomatology, Shanghai, China; Shanghai Research Institute of Stomatology, Shanghai, China; Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Chun Xu
- Department of Prosthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; College of Stomatology, Shanghai Jiao Tong University, Shanghai, China; National Center for Stomatology, Shanghai, China; National Clinical Research Center for Oral Diseases, Shanghai, China; Shanghai Key Laboratory of Stomatology, Shanghai, China; Shanghai Research Institute of Stomatology, Shanghai, China.
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