Division of Ophthalmology Informatics and Data Science, The Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, 9415 Campus Point Drive, La Jolla, CA 92037, USA; Jacobs Retina Center, 9415 Campus Point Drive, La Jolla, CA 92037, USA.
Division of Ophthalmology Informatics and Data Science, The Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, 9415 Campus Point Drive, La Jolla, CA 92037, USA; Division of Biomedical Informatics, Department of Medicine, University of California San Diego Health System, University of California San Diego, La Jolla, CA, USA.
Asia Pac J Ophthalmol (Phila). 2024 Jul-Aug;13(4):100089. doi: 10.1016/j.apjo.2024.100089. Epub 2024 Aug 10.
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.
A literature review and analysis of current AI applications and educational programs in ophthalmology.
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.
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 However, challenges such as AI hallucinations, biases, lack of interpretability, and outdated training data limit clinical deployment.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.
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.
探索生成式人工智能，特别是大型语言模型（LLM）在眼科学教育和实践中的整合，探讨其应用、益处、挑战和未来方向。
对眼科学中人工智能应用和教育计划的文献进行综述和分析。
分析发表的研究、综述、文章、网站和机构关于 AI 在眼科学中应用的报告。考察纳入 AI 的教育计划，包括课程框架、培训方法以及对 AI 在医学检查和临床病例研究中的表现进行评估。
生成式 AI，特别是 LLM，显示出在眼科学中提高诊断准确性和改善患者护理的潜力。应用包括辅助患者、医生和医学生的教育。然而，AI 幻觉、偏差、缺乏可解释性和过时的训练数据等挑战限制了其临床部署。研究表明，LLM 在眼科委员会考试问题上的准确性存在差异，突出了对更可靠 AI 整合的需求。全国范围内的几个教育计划提供与临床医学和眼科学相关的 AI 和数据科学培训。
生成式 AI 和 LLM 为眼科学教育和实践带来了有前景的进展。通过包含基本 AI 原理、道德准则和更新、无偏差的训练数据的全面课程来应对挑战至关重要。未来的方向包括开发与临床相关的评估指标、实施具有人工监督的混合模型、利用丰富的图像数据以及将 AI 性能与眼科医生进行基准测试。制定关于数据隐私、安全和透明度的稳健政策对于在眼科学中培养 AI 应用的安全和道德环境至关重要。
