目的:探索生成AI的整合,特别是大型语言模型(LLM),在眼科教育和实践中,解决他们的应用,好处,挑战,和未来的方向。
方法:对当前AI在眼科中的应用和教育计划进行文献回顾和分析。
方法:对已发表研究的分析,reviews,文章,网站,以及有关AI在眼科中使用的机构报告。结合AI的教育计划的检查,包括课程框架,训练方法,以及人工智能在医学检查和临床案例研究中的表现评价。
结果:生成AI,特别是LLM,显示出提高眼科诊断准确性和患者护理的潜力。应用包括帮助患者,内科医生,和医学生的教育。然而,诸如人工智能幻觉之类的挑战,偏见,缺乏可解释性,和过时的培训数据限制了临床部署。研究表明,眼科委员会考试问题的LLM准确性不同,强调需要更可靠的人工智能集成。全国范围内的一些教育计划提供与临床医学和眼科相关的AI和数据科学培训。
结论:生成AI和LLM在眼科教育和实践方面提供了有希望的进步。通过包括基本人工智能原则的综合课程应对挑战,道德准则,并更新,无偏见的训练数据至关重要。未来的方向包括开发临床相关的评估指标,在人为监督下实施混合模型,利用图像丰富的数据,并将AI性能与眼科医生进行基准测试。关于数据隐私的强有力的政策,安全,和透明度对于促进AI在眼科应用的安全和道德环境至关重要。
OBJECTIVE: 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.
METHODS: 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 However, challenges such as AI hallucinations, biases, lack of interpretability, and outdated training data limit clinical deployment.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.