Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: A head-to-head cross-sectional study

• Published in: PLOS digital health 2024-04, Vol.3 (4), p.e0000341
• Main Authors: Arun James Thirunavukarasu, Shathar Mahmood, Andrew Malem, William Paul Foster, Rohan Sanghera, Refaat Hassan, Sean Zhou, Shiao Wei Wong, Yee Ling Wong, Yu Jeat Chong, Abdullah Shakeel, Yin-Hsi Chang, Benjamin Kye Jyn Tan, Nikhil Jain, Ting Fang Tan, Saaeha Rauz, Daniel Shu Wei Ting, Darren Shu Jeng Ting
• Format: Article
• Language: English
• ISSN: 2767-3170
• DOI: 10.1371/journal.pdig.0000341

Large language models (LLMs) underlie remarkable recent advanced in natural language processing, and they are beginning to be applied in clinical contexts. We aimed to evaluate the clinical potential of state-of-the-art LLMs in ophthalmology using a more robust benchmark than raw examination scores. We trialled GPT-3.5 and GPT-4 on 347 ophthalmology questions before GPT-3.5, GPT-4, PaLM 2, LLaMA, expert ophthalmologists, and doctors in training were trialled on a mock examination of 87 questions. Performance was analysed with respect to question subject and type (first order recall and higher order reasoning). Masked ophthalmologists graded the accuracy, relevance, and overall preference of GPT-3.5 and GPT-4 responses to the same questions. The performance of GPT-4 (69%) was superior to GPT-3.5 (48%), LLaMA (32%), and PaLM 2 (56%). GPT-4 compared favourably with expert ophthalmologists (median 76%, range 64-90%), ophthalmology trainees (median 59%, range 57-63%), and unspecialised junior doctors (median 43%, range 41-44%). Low agreement between LLMs and doctors reflected idiosyncratic differences in knowledge and reasoning with overall consistency across subjects and types (p>0.05). All ophthalmologists preferred GPT-4 responses over GPT-3.5 and rated the accuracy and relevance of GPT-4 as higher (p