Artiaga Jose Carlo M, Guevarra Ma Carmela B, Sosuan George Michael N, Agnihotri Akshay Prashant, Nagel Ines Doris, Kalaw Fritz Gerald P
Department of Ophthalmology and Visual Sciences, Philippine General Hospital, University of the Philippines Manila, Manila City, Philippines.
Massachusetts Eye and Ear, Boston, MA, USA.
Eye (Lond). 2025 Aug 25. doi: 10.1038/s41433-025-03935-7.
Since its introduction in November 2022, the public interest in the utility of large language models (LLMs) has gained widespread adoption among individual consumers and among medical practitioners, with a consequent increase in publications describing their utility in healthcare. This review highlights original research articles on how LLM's can be utilized by various stakeholders in ophthalmology through clinical assistance, patient education, medical education, and research. ChatGPT consistently responds with better accuracy and quality than other LLMs across various studies employing different methodologies, with newer iterations offering more advantages. Studies have likewise identified limitations of LLMs, which include hallucination, inability to interpret image-based prompts, and limited performance across non-English languages. As newer iterations of available and more advanced models with image processing are currently being introduced, generative artificial intelligence should be continuously monitored for its implications in eye care.
自2022年11月推出以来,公众对大语言模型(LLMs)效用的兴趣在个人消费者和医学从业者中得到了广泛应用,随之而来的是描述其在医疗保健中效用的出版物有所增加。本综述重点介绍了关于大语言模型如何通过临床辅助、患者教育、医学教育和研究被眼科领域的不同利益相关者所利用的原创研究文章。在采用不同方法的各种研究中,ChatGPT的回答始终比其他大语言模型具有更高的准确性和质量,更新的版本具有更多优势。研究同样也发现了大语言模型的局限性,包括幻觉、无法解释基于图像的提示以及在非英语语言方面的表现有限。随着目前正在引入具有图像处理功能的更新的可用且更先进的模型版本,应持续监测生成式人工智能对眼保健的影响。