{"title":"人工智能时代的眼科教育回顾。","authors":"","doi":"10.1016/j.apjo.2024.100089","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>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.</p></div><div><h3>Design</h3><p>A literature review and analysis of current AI applications and educational programs in ophthalmology.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>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 However, challenges such as AI hallucinations, biases, lack of interpretability, and outdated training data limit clinical deployment.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.</p></div><div><h3>Conclusions</h3><p>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.</p></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000902/pdfft?md5=c732bd60e43fb37cd5168b2517837774&pid=1-s2.0-S2162098924000902-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A review of ophthalmology education in the era of generative artificial intelligence\",\"authors\":\"\",\"doi\":\"10.1016/j.apjo.2024.100089\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>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.</p></div><div><h3>Design</h3><p>A literature review and analysis of current AI applications and educational programs in ophthalmology.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusions</h3><p>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.</p></div>\",\"PeriodicalId\":8594,\"journal\":{\"name\":\"Asia-Pacific Journal of Ophthalmology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2162098924000902/pdfft?md5=c732bd60e43fb37cd5168b2517837774&pid=1-s2.0-S2162098924000902-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Asia-Pacific Journal of Ophthalmology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2162098924000902\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asia-Pacific Journal of Ophthalmology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2162098924000902","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
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.
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.