Comparison of Large Language Models in Diagnosis and Management of Challenging Clinical Cases.

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Compare large language models (LLMs) in analyzing and responding to a difficult series of ophthalmic cases. A comparative case series involving LLMs that met inclusion criteria tested on twenty difficult case studies posed in open-text format. Fifteen LLMs accessible to ophthalmologists were tested against twenty case studies published in JAMA Ophthalmology. Each case was presented in identical, open-ended text fashion to each LLM and open-ended responses regarding differential diagnosis, next diagnostic tests and recommended treatments were requested. Responses were recorded and assessed for accuracy against published correct answers. The main outcome was accuracy of LLMs against the correct answers. Secondary outcomes included comparative performance on the differential diagnosis, ancillary testing, and treatment subtests; and readability of responses. Scores were normally distributed and ranged from 0-35 (with a maximum score of 60) with a mean ± standard deviation of 19 ± 9. Scores for three of the LLMs (ChatGPT 3.5, Claude Pro, and Copilot Pro) were statistically significantly higher than the mean. Two of the high-performing LLMs were paid subscription (Claude Pro and Copilot Pro) and one was free (ChatGPT 3.5). While there were no clinical or statistical differences between ChatGPT 3.5 and Claude Pro, a separation of +5 points, or 0.56 standard deviations, between Copilot Pro and the other highly ranked LLMs was present. Readability of all tested programs were above the AMA (American Medical Association) reading level recommendations to public consumers of eight grade. Subscription LLMs were more prevalent among highly ranked LLMs suggesting that these perform better as ophthalmic assistants. While readability was poor for the average person, the content was understood by a board-certified ophthalmologist. The accuracy of LLMs is not high enough to recommend patient care in standalone mode, but aiding clinicians in patient care and prevent oversights is promising.