Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA.
Curr Opin Ophthalmol. 2023 May 1;34(3):261-266. doi: 10.1097/ICU.0000000000000939. Epub 2022 Dec 29.
Assistive (nonautonomous) artificial intelligence (AI) models designed to support (rather than function independently of) clinicians have received increasing attention in medicine. This review aims to highlight several recent developments in these models over the past year and their ophthalmic implications.
Artificial intelligence models with a diverse range of applications in ophthalmology have been reported in the literature over the past year. Many of these systems have reported high performance in detection, classification, prognostication, and/or monitoring of retinal, glaucomatous, anterior segment, and other ocular pathologies.
Over the past year, developments in AI have been made that have implications affecting ophthalmic surgical training and refractive outcomes after cataract surgery, therapeutic monitoring of disease, disease classification, and prognostication. Many of these recently developed models have obtained encouraging results and have the potential to serve as powerful clinical decision-making tools pending further external validation and evaluation of their generalizability.
旨在辅助（非自主）临床医生的人工智能（AI）模型在医学领域受到越来越多的关注。本综述旨在强调过去一年中这些模型的几个最新进展及其在眼科领域的意义。
过去一年的文献中报道了 AI 在眼科领域具有多种应用的模型。这些系统中的许多报告在检测、分类、预后和/或监测视网膜、青光眼、前节和其他眼部疾病方面具有出色的性能。
过去一年来，AI 的发展对眼科手术培训和白内障手术后屈光结果、疾病的治疗监测、疾病分类和预后产生了影响。许多这些新开发的模型已经取得了令人鼓舞的结果，并有可能成为强大的临床决策工具，有待进一步的外部验证和对其通用性的评估。
