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ChatGPT and Beyond: An overview of the growing field of large language models and their use in ophthalmology

Abstract

ChatGPT, an artificial intelligence (AI) chatbot built on large language models (LLMs), has rapidly gained popularity. The benefits and limitations of this transformative technology have been discussed across various fields, including medicine. The widespread availability of ChatGPT has enabled clinicians to study how these tools could be used for a variety of tasks such as generating differential diagnosis lists, organizing patient notes, and synthesizing literature for scientific research. LLMs have shown promising capabilities in ophthalmology by performing well on the Ophthalmic Knowledge Assessment Program, providing fairly accurate responses to questions about retinal diseases, and in generating differential diagnoses list. There are current limitations to this technology, including the propensity of LLMs to “hallucinate”, or confidently generate false information; their potential role in perpetuating biases in medicine; and the challenges in incorporating LLMs into research without allowing “AI-plagiarism” or publication of false information. In this paper, we provide a balanced overview of what LLMs are and introduce some of the LLMs that have been generated in the past few years. We discuss recent literature evaluating the role of these language models in medicine with a focus on ChatGPT. The field of AI is fast-paced, and new applications based on LLMs are being generated rapidly; therefore, it is important for ophthalmologists to be aware of how this technology works and how it may impact patient care. Here, we discuss the benefits, limitations, and future advancements of LLMs in patient care and research.

摘要

基于大型语言模型(LLM)的人工智能聊天机器人ChatGPT已迅速普及。这项变革性技术的优势和局限性在包括医学在内的各个领域引致了广泛讨论。ChatGPT的广泛应用使得临床医生能够将这些工具用于各种任务, 例如生成鉴别诊断清单、整理病人记录以及为科学研究整合文献。LLM通过在眼科知识评估项目表现良好、对视网膜疾病问题可提供准确的回答, 并在鉴别诊断方面显示出在眼科领域应用前景的能力。这项技术目前存在局限性, 包括LLM的“幻觉”倾向, 或自信地生成虚假信息; 在医学偏倚方面存在潜在作用; 以及在不允许“人工智能剽窃”或发表虚假信息的情况下将LLM纳入研究等方面的挑战。在本文中, 我们对什么是LLM提供中立的概述, 介绍了过去几年产生的一些LLM。我们讨论了评估这些语言模型在医学中作用的最新文献, 重点是ChatGPT。人工智能领域发展快速, 基于LLM的新的应用层出不穷; 因此, 眼科医生了解这项技术的工作原理以及它对患者治疗的影响非常重要。在此, 我们对LLM在患者治疗和研究方面的优势、局限性及未来发展进行讨论。

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Fig. 1: ChatGPT in different languages for ophthalmic pathology.
Fig. 2: ChatGPT is provided with deidentified data from a glaucoma database [67].

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Acknowledgements

The authors would like to thank their colleagues who helped test SightBot.

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Correspondence to Jay Chhablani.

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The custom-generated chatbot mentioned in this article, SightBot, was developed by Suvansh Sanjeev and Nikita Kedia under advisement from Jay Chhablani, and was used as a demo for Suvansh’s company, Brilliantly AI (https://brilliantly.ai). The chatbot is not revenue generating.

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Kedia, N., Sanjeev, S., Ong, J. et al. ChatGPT and Beyond: An overview of the growing field of large language models and their use in ophthalmology. Eye (2024). https://doi.org/10.1038/s41433-023-02915-z

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