TY - JOUR
T1 - Evaluating the Diagnostic Accuracy and Management Recommendations of ChatGPT in Uveitis
AU - Rojas-Carabali, William
AU - Cifuentes-González, Carlos
AU - Wei, Xin
AU - Putera, Ikhwanuliman
AU - Sen, Alok
AU - Thng, Zheng Xian
AU - Agrawal, Rajdeep
AU - Elze, Tobias
AU - Sobrin, Lucia
AU - Kempen, John H.
AU - Lee, Bernett
AU - Biswas, Jyotirmay
AU - Nguyen, Quan Dong
AU - Gupta, Vishali
AU - de-la-Torre, Alejandra
AU - Agrawal, Rupesh
N1 - Publisher Copyright:
© 2023 Taylor & Francis Group, LLC.
PY - 2023/9/18
Y1 - 2023/9/18
N2 - 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.
AB - 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.
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U2 - 10.1080/09273948.2023.2253471
DO - 10.1080/09273948.2023.2253471
M3 - Article
C2 - 37722842
AN - SCOPUS:85171542097
SN - 0927-3948
JO - Ocular Immunology and Inflammation
JF - Ocular Immunology and Inflammation
ER -
