The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally.

计算机图形处理单元的出现，数学模型的改进以及大数据的可用性，使得使用机器学习（ML）和深度学习（DL）技术的人工智能（AI）能够在社交媒体，社交媒体等广泛应用中实现强大的性能。物联网，汽车工业和医疗保健。DL系统尤其在图像，语音和运动识别以及自然语言处理方面提供了改进的功能。在医学上，已经在以图像为中心的专科领域（例如放射科，皮肤科，病理科和眼科科）证明了AI和DL系统的显着进步。包括预先注册的前瞻性临床试验在内的新研究表明，DL系统在检测糖尿病性视网膜病变（DR），青光眼，年龄相关性黄斑变性（AMD），早产儿视网膜病变，屈光不正以及从心血管眼底照片中识别心血管危险因素和疾病。使用光学相干断层扫描（OCT）识别AI和DL系统以识别视网膜疾病（例如新生血管AMD和糖尿病性黄斑水肿）的疾病特征，进展和治疗反应也越来越受到关注。此外，将ML应用于视野可能有助于检测青光眼的进展。仅有有限的研究在AL和DL算法中纳入了包括电子健康记录在内的临床数据，并且尚无前瞻性研究来证明AI和DL算法可以预测临床眼病的发展。本文介绍了全球眼疾的负担，AI和DL系统可能适用的未满足需求和具有公共卫生重要性的常见条件。讨论了建立满足这些需求的DL系统的技术和临床方面，以及在临床采用方面的潜在挑战。AI，ML和DL可能会在临床眼科实践中发挥至关重要的作用，这将对全球人口老龄化背景下视力障碍的主要原因的筛查，诊断和随访产生影响。