Interpretation of Clinical Retinal Images Using an Artificial Intelligence Chatbot

Abstract

PurposeTo assess the performance of ChatGPT-4 in providing accurate diagnoses to retina teaching cases from OCTCases. DesignCross-sectional study. SubjectsRetina teaching cases from OCTCases. MethodsWe prompted a custom chatbot with 69 retina cases containing multimodal ophthalmic images, asking it to provide the most likely diagnosis. In a sensitivity analysis, we inputted increasing amounts of clinical information pertaining to each case until the chatbot achieved a correct diagnosis. We performed multivariable logistic regressions on Stata v17.0 (StataCorp LLC, College Station, Texas) to investigate associations between the amount of text-based information inputted per prompt and the odds of the chatbot achieving a correct diagnosis, adjusting for the laterality of cases, number of ophthalmic images inputted, and imaging modalities. Main Outcome MeasuresOur primary outcome was the proportion of cases for which the chatbot was able to provide a correct diagnosis. Our secondary outcome was the chatbot’s performance in relation to the amount of text-based information accompanying ophthalmic images. ResultsAcross 69 retina cases collectively containing 139 ophthalmic images, the chatbot was able to provide a definitive, correct diagnosis for 35 (50.7%) cases. The chatbot needed variable amounts of clinical information to achieve a correct diagnosis, where the entire patient description as presented by OCTCases was required for a majority of correctly diagnosed cases (23/35 cases, 65.7%). Relative to when the chatbot was only prompted with a patient’s age and sex, the chatbot achieved a higher odds of a correct diagnosis when prompted with an entire patient description (OR=10.1, 95%CI=[3.3, 30.3], p<0.01). Despite providing an incorrect diagnosis for 34 (49.3%) cases, the chatbot listed the correct diagnosis within its differential diagnosis for 7 (20.6%) of these incorrectly answered cases. ConclusionsThis custom chatbot was able to accurately diagnose approximately half of the retina cases requiring multimodal input, albeit relying heavily on text-based contextual information that accompanied ophthalmic images. The diagnostic ability of the chatbot in interpretation of multimodal imaging without text-based information is currently limited. The appropriate use of the chatbot in this setting is of utmost importance, given bioethical concerns.