Performance of popular large language models in glaucoma patient education: A randomized controlled study - Yuyu Cao; Wei Lu; Runhan Shi; Fuying Liu; Steven Liu; Xinwei Xu; Jin Yang; Guangyu Rong; Changchang Xin; Xujiao Zhou; Xinghuai Sun; Jiaxu Hong (Advances in Ophthalmology Practice and Research)
Purpose: The advent of chatbots based on large language models (LLMs), such as ChatGPT, has significantly transformed knowledge acquisition. However, the application of LLMs in glaucoma patient education remains elusive. In this study, we comprehensively compared the performance of four common LLMs – Qwen, Baichuan 2, ChatGPT-4o, and PaLM 2 – in the context of glaucoma patient education. Methods: Initially, senior ophthalmologists were asked with scoring responses generated by the LLMs, which were answers to the most frequent glaucoma-related questions posed by patients. The Chinese Readability Platform was employed to assess the recommended reading age and reading difficulty score of the four LLMs. Subsequently, optimized models were filtered, and 29 glaucoma patients participated in posing questions to the chatbots and scoring the answers within a real-world clinical setting. Attending ophthalmologists were also required to score the answers across five dimensions: correctness, completeness, readability, helpfulness, and safety. Patients, on the other hand, scored the answers based on three dimensions: satisfaction, readability, and helpfulness. Results: In the first stage, Baichuan 2 and ChatGPT-4o outperformed the other two models, though ChatGPT-4o had higher recommended reading age and reading difficulty scores. In the second stage, both Baichuan 2 and ChatGPT-4o demonstrated exceptional performance among patients and ophthalmologists, with no statistically significant differences observed. Conclusions: Our research identifies Baichuan 2 and ChatGPT-4o as prominent LLMs, offering viable options for glaucoma education.