William Rojas-Carabali, Carlos Cifuentes-González, Xin Wei, Ikhwanuliman Putera, Alok Sen, Zheng Xian Thng, Rajdeep Agrawal, Tobias Elze, Lucia Sobrin, John H Kempen, Bernett Lee, Jyotirmay Biswas, Quan Dong Nguyen, Vishali Gupta, Alejandra de-la-Torre, Rupesh Agrawal
{"title":"评估 ChatGPT 对葡萄膜炎的诊断准确性和管理建议。","authors":"William Rojas-Carabali, Carlos Cifuentes-González, Xin Wei, Ikhwanuliman Putera, Alok Sen, Zheng Xian Thng, Rajdeep Agrawal, Tobias Elze, Lucia Sobrin, John H Kempen, Bernett Lee, Jyotirmay Biswas, Quan Dong Nguyen, Vishali Gupta, Alejandra de-la-Torre, Rupesh Agrawal","doi":"10.1080/09273948.2023.2253471","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Accurate diagnosis and timely management are vital for favorable uveitis outcomes. Artificial Intelligence (AI) holds promise in medical decision-making, particularly in ophthalmology. Yet, the diagnostic precision and management advice from AI-based uveitis chatbots lack assessment.</p><p><strong>Methods: </strong>We appraised diagnostic accuracy and management suggestions of an AI-based chatbot, ChatGPT, versus five uveitis-trained ophthalmologists, using 25 standard cases aligned with new Uveitis Nomenclature guidelines. Participants predicted likely diagnoses, two differentials, and next management steps. Comparative success rates were computed.</p><p><strong>Results: </strong>Ophthalmologists excelled (60-92%) in likely diagnosis, exceeding AI (60%). Considering fully and partially accurate diagnoses, ophthalmologists achieved 76-100% success; AI attained 72%. Despite an 8% AI improvement, its overall performance lagged. Ophthalmologists and AI agreed on diagnosis in 48% cases, with 91.6% exhibiting concurrence in management plans.</p><p><strong>Conclusions: </strong>The study underscores AI chatbots' potential in uveitis diagnosis and management, indicating their value in reducing diagnostic errors. Further research is essential to enhance AI chatbot precision in diagnosis and recommendations.</p>","PeriodicalId":19406,"journal":{"name":"Ocular Immunology and Inflammation","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluating the Diagnostic Accuracy and Management Recommendations of ChatGPT in Uveitis.\",\"authors\":\"William Rojas-Carabali, Carlos Cifuentes-González, Xin Wei, Ikhwanuliman Putera, Alok Sen, Zheng Xian Thng, Rajdeep Agrawal, Tobias Elze, Lucia Sobrin, John H Kempen, Bernett Lee, Jyotirmay Biswas, Quan Dong Nguyen, Vishali Gupta, Alejandra de-la-Torre, Rupesh Agrawal\",\"doi\":\"10.1080/09273948.2023.2253471\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Accurate diagnosis and timely management are vital for favorable uveitis outcomes. Artificial Intelligence (AI) holds promise in medical decision-making, particularly in ophthalmology. Yet, the diagnostic precision and management advice from AI-based uveitis chatbots lack assessment.</p><p><strong>Methods: </strong>We appraised diagnostic accuracy and management suggestions of an AI-based chatbot, ChatGPT, versus five uveitis-trained ophthalmologists, using 25 standard cases aligned with new Uveitis Nomenclature guidelines. Participants predicted likely diagnoses, two differentials, and next management steps. Comparative success rates were computed.</p><p><strong>Results: </strong>Ophthalmologists excelled (60-92%) in likely diagnosis, exceeding AI (60%). Considering fully and partially accurate diagnoses, ophthalmologists achieved 76-100% success; AI attained 72%. Despite an 8% AI improvement, its overall performance lagged. Ophthalmologists and AI agreed on diagnosis in 48% cases, with 91.6% exhibiting concurrence in management plans.</p><p><strong>Conclusions: </strong>The study underscores AI chatbots' potential in uveitis diagnosis and management, indicating their value in reducing diagnostic errors. Further research is essential to enhance AI chatbot precision in diagnosis and recommendations.</p>\",\"PeriodicalId\":19406,\"journal\":{\"name\":\"Ocular Immunology and Inflammation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ocular Immunology and Inflammation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/09273948.2023.2253471\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/9/18 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ocular Immunology and Inflammation","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/09273948.2023.2253471","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/9/18 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
Evaluating the Diagnostic Accuracy and Management Recommendations of ChatGPT in Uveitis.
Introduction: Accurate diagnosis and timely management are vital for favorable uveitis outcomes. Artificial Intelligence (AI) holds promise in medical decision-making, particularly in ophthalmology. Yet, the diagnostic precision and management advice from AI-based uveitis chatbots lack assessment.
Methods: We appraised diagnostic accuracy and management suggestions of an AI-based chatbot, ChatGPT, versus five uveitis-trained ophthalmologists, using 25 standard cases aligned with new Uveitis Nomenclature guidelines. Participants predicted likely diagnoses, two differentials, and next management steps. Comparative success rates were computed.
Results: Ophthalmologists excelled (60-92%) in likely diagnosis, exceeding AI (60%). Considering fully and partially accurate diagnoses, ophthalmologists achieved 76-100% success; AI attained 72%. Despite an 8% AI improvement, its overall performance lagged. Ophthalmologists and AI agreed on diagnosis in 48% cases, with 91.6% exhibiting concurrence in management plans.
Conclusions: The study underscores AI chatbots' potential in uveitis diagnosis and management, indicating their value in reducing diagnostic errors. Further research is essential to enhance AI chatbot precision in diagnosis and recommendations.
期刊介绍：
Ocular Immunology & Inflammation ranks 18 out of 59 in the Ophthalmology Category.Ocular Immunology and Inflammation is a peer-reviewed, scientific publication that welcomes the submission of original, previously unpublished manuscripts directed to ophthalmologists and vision scientists. Published bimonthly, the journal provides an international medium for basic and clinical research reports on the ocular inflammatory response and its control by the immune system. The journal publishes original research papers, case reports, reviews, letters to the editor, meeting abstracts, and invited editorials.
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