人工智能时代的眼科教育回顾。

IF 3.7 3区 医学 Q1 OPHTHALMOLOGY Asia-Pacific Journal of Ophthalmology Pub Date : 2024-07-01 DOI:10.1016/j.apjo.2024.100089
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引用次数: 0

摘要

目的:探讨生成式人工智能,特别是大型语言模型(LLMs)在眼科教育和实践中的整合,探讨其应用、益处、挑战和未来方向:设计:对当前人工智能在眼科领域的应用和教育项目进行文献综述和分析:分析已发表的有关人工智能在眼科中应用的研究、评论、文章、网站和机构报告。检查包含人工智能的教育项目,包括课程框架、培训方法以及人工智能在医学考试和临床案例研究中的表现评估:生成式人工智能,尤其是 LLM,显示出提高眼科诊断准确性和患者护理的潜力。其应用包括帮助病人、医生和医科学生接受教育。然而,人工智能的幻觉、偏差、缺乏可解释性以及训练数据过时等挑战限制了临床应用。研究显示,LLM 对眼科医学考试题的准确性参差不齐,这突出表明需要更可靠的人工智能集成。全国有多个教育项目提供与临床医学和眼科学相关的人工智能和数据科学培训:结论:生成式人工智能和 LLM 为眼科教育和实践带来了充满希望的进步。通过包括基本人工智能原则、道德准则和最新、无偏见的培训数据在内的综合课程来应对挑战至关重要。未来的发展方向包括制定与临床相关的评估指标、在人工监督下实施混合模型、利用图像丰富的数据以及以眼科医生为基准来衡量人工智能的性能。健全的数据隐私、安全和透明度政策对于为眼科领域的人工智能应用营造一个安全、道德的环境至关重要。
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A review of ophthalmology education in the era of generative artificial intelligence

Purpose

To explore the integration of generative AI, specifically large language models (LLMs), in ophthalmology education and practice, addressing their applications, benefits, challenges, and future directions.

Design

A literature review and analysis of current AI applications and educational programs in ophthalmology.

Methods

Analysis of published studies, reviews, articles, websites, and institutional reports on AI use in ophthalmology. Examination of educational programs incorporating AI, including curriculum frameworks, training methodologies, and evaluations of AI performance on medical examinations and clinical case studies.

Results

Generative AI, particularly LLMs, shows potential to improve diagnostic accuracy and patient care in ophthalmology. Applications include aiding in patient, physician, and medical students’ education. However, challenges such as AI hallucinations, biases, lack of interpretability, and outdated training data limit clinical deployment. Studies revealed varying levels of accuracy of LLMs on ophthalmology board exam questions, underscoring the need for more reliable AI integration. Several educational programs nationwide provide AI and data science training relevant to clinical medicine and ophthalmology.

Conclusions

Generative AI and LLMs offer promising advancements in ophthalmology education and practice. Addressing challenges through comprehensive curricula that include fundamental AI principles, ethical guidelines, and updated, unbiased training data is crucial. Future directions include developing clinically relevant evaluation metrics, implementing hybrid models with human oversight, leveraging image-rich data, and benchmarking AI performance against ophthalmologists. Robust policies on data privacy, security, and transparency are essential for fostering a safe and ethical environment for AI applications in ophthalmology.

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来源期刊
CiteScore
8.10
自引率
18.20%
发文量
197
审稿时长
6 weeks
期刊介绍: The Asia-Pacific Journal of Ophthalmology, a bimonthly, peer-reviewed online scientific publication, is an official publication of the Asia-Pacific Academy of Ophthalmology (APAO), a supranational organization which is committed to research, training, learning, publication and knowledge and skill transfers in ophthalmology and visual sciences. The Asia-Pacific Journal of Ophthalmology welcomes review articles on currently hot topics, original, previously unpublished manuscripts describing clinical investigations, clinical observations and clinically relevant laboratory investigations, as well as .perspectives containing personal viewpoints on topics with broad interests. Editorials are published by invitation only. Case reports are generally not considered. The Asia-Pacific Journal of Ophthalmology covers 16 subspecialties and is freely circulated among individual members of the APAO’s member societies, which amounts to a potential readership of over 50,000.
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