Agnihotri Akshay Prashant, Nagel Ines Doris, Artiaga Jose Carlo M, Guevarra Ma Carmela B, Sosuan George Michael N, Kalaw Fritz Gerald P
Jacobs Retina Center, University of California, San Diego, La Jolla, California.
Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California.
Ophthalmol Sci. 2024 Dec 17;5(3):100681. doi: 10.1016/j.xops.2024.100681. eCollection 2025 May-Jun.
To review and evaluate the current literature on the application and impact of large language models (LLMs) in the field of ophthalmology, focusing on studies published in high-ranking ophthalmology journals.
This is a retrospective review of published articles.
This study did not involve human participation.
Articles published in the first quartile (Q1) of ophthalmology journals on Scimago Journal & Country Rank discussing different LLMs up to June 7, 2024, were reviewed, parsed, and analyzed.
All available articles were parsed and analyzed, which included the article and author characteristics and data regarding the LLM used and its applications, focusing on its use in medical education, clinical assistance, research, and patient education.
There were 35 Q1-ranked journals identified, 19 of which contained articles discussing LLMs, with 101 articles eligible for review. One-third were original investigations (32%; 32/101), with an average of 5.3 authors per article. The United States (50.4%; 51/101) was the most represented country, followed by the United Kingdom (25.7%; 26/101) and Canada (16.8%; 17/101). ChatGPT was the most used LLM among the studies, with different versions discussed and compared. Large language model applications were discussed relevant to their implications in medical education, clinical assistance, research, and patient education.
The numerous publications on the use of LLM in ophthalmology can provide valuable insights for stakeholders and consumers of these applications. Large language models present significant opportunities for advancement in ophthalmology, particularly in team science, education, clinical assistance, and research. Although LLMs show promise, they also show challenges such as performance inconsistencies, bias, and ethical concerns. The study emphasizes the need for ongoing artificial intelligence improvement, ethical guidelines, and multidisciplinary collaboration.
The author(s) have no proprietary or commercial interest in any materials discussed in this article.
回顾和评估当前关于大语言模型（LLMs）在眼科领域应用及影响的文献，重点关注发表于高排名眼科期刊上的研究。
这是一项对已发表文章的回顾性研究。
本研究未涉及人类参与。
对截至2024年6月7日发表在Scimago期刊与国家排名中眼科期刊第一四分位数（Q1）上讨论不同大语言模型的文章进行回顾、解析和分析。
对所有可得文章进行解析和分析，包括文章及作者特征，以及有关所使用大语言模型及其应用的数据，重点关注其在医学教育、临床辅助、研究和患者教育方面的应用。
共识别出35种Q1排名期刊，其中19种包含讨论大语言模型的文章，有101篇文章符合审查条件。三分之一为原创研究（32%；32/101），每篇文章平均有5.3位作者。美国是占比最高的国家（50.4%；51/101），其次是英国（25.7%；26/101）和加拿大（16.8%；17/101）。ChatGPT是研究中使用最多的大语言模型，讨论并比较了不同版本。探讨了大语言模型应用在医学教育、临床辅助、研究和患者教育方面的影响。
众多关于大语言模型在眼科应用的出版物可为这些应用的利益相关者和使用者提供有价值的见解。大语言模型为眼科发展带来了重大机遇，尤其是在团队科学、教育、临床辅助和研究方面。尽管大语言模型显示出前景，但它们也存在诸如性能不一致、偏差和伦理问题等挑战。该研究强调了持续改进人工智能、制定伦理准则和开展多学科合作的必要性。
作者对本文讨论的任何材料均无所有权或商业利益。
