Evaluating the Performance of ChatGPT in Ophthalmology

Abstract

Purpose: Foundation models are a novel type of artificial intelligence algorithms, in which models are pretrained at scale on unannotated data and fine-tuned for a myriad of downstream tasks, such as generating text. This study assessed the accuracy of ChatGPT, a large language model (LLM), in the ophthalmology question-answering space. Design: Evaluation of diagnostic test or technology. Participants: ChatGPT is a publicly available LLM. Methods: We tested 2 versions of ChatGPT (January 9 "legacy" and ChatGPT Plus) on 2 popular multiple choice question banks commonly used to prepare for the high-stakes Ophthalmic Knowledge Assessment Program (OKAP) examination. We generated two 260-question simulated exams from the Basic and Clinical Science Course (BCSC) Self-Assessment Program and the OphthoQuestions online question bank. We carried out logistic regression to determine the effect of the examination section, cognitive level, and difficulty index on answer accuracy. We also performed a post hoc analysis using Tukey's test to decide if there were meaningful differences between the tested subspecialties. Main Outcome Measures: value of < 0.05. Results: = 0.029), similar post hoc findings were not seen with ChatGPT Plus, suggesting more consistent results across examination sections. Conclusion: ChatGPT has encouraging performance on a simulated OKAP examination. Specializing LLMs through domain-specific pretraining may be necessary to improve their performance in ophthalmic subspecialties. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

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