Tran Jenny, Estevez Jose J, Howard Natasha J, Kumar Saravana
College of Medicine and Public Health, Flinders University, Adelaide, Australia.
Department of Ophthalmology, Flinders Centre for Ophthalmology, Eye and Vision Research, Flinders University, Adelaide, Australia.
Clin Exp Ophthalmol. 2025 Jun 14. doi: 10.1111/ceo.14567.
Diabetic retinopathy is a leading cause of preventable blindness worldwide. Meanwhile, artificial intelligence is rapidly growing in clinical utility within medicine. This scoping review aims to identify and summarise existing literature on the barriers and enablers of clinical applications of artificial intelligence systems for the screening of diabetic retinopathy.
Utilising a systematic approach and the PRISMA-ScR protocol for conducting scoping reviews, searches were performed in MEDLINE, Embase, Emcare, Cochrane, CINAHL, ProQuest, Scopus and grey literature (Australian Indigenous Health InfoNet). Two reviewers independently reviewed the records. A third reviewer provided consensus. Data extraction and synthesis in narrative form ensued.
A total of 3844 articles were screened, of which 18 were selected. Published between 2018 and 2023, the selected studies varied in study design and were conducted across 10 countries. Several barriers and enablers were identified and categorised into four domains: healthcare system, healthcare professional, healthcare user and information technology. Within the healthcare system, clinical efficiency was reported on most frequently. Concerning the healthcare professional, education was most frequently discussed. Within healthcare user, studies most frequently identified factors pertaining to patient outcomes, while diagnostic performance was most frequently explored under the information technology domain.
As evidence for the efficacy of artificial intelligence for diabetic retinopathy screening grows, barriers to and enablers for its uptake in clinical practice are paramount considerations. Translating the knowledge of systems, provider, consumer and technological factors informs clinical strategies, ultimately facilitating the sustainable and effective implementation of this novel technology for screening practices.
糖尿病视网膜病变是全球可预防失明的主要原因。与此同时，人工智能在医学临床应用中的发展迅速。本综述旨在识别和总结关于人工智能系统在糖尿病视网膜病变筛查临床应用中的障碍和促进因素的现有文献。
采用系统方法和PRISMA-ScR方案进行综述，在MEDLINE、Embase、Emcare、Cochrane、CINAHL、ProQuest、Scopus和灰色文献（澳大利亚原住民健康信息网）中进行检索。两名评审员独立评审记录。第三名评审员提供共识。随后以叙述形式进行数据提取和综合。
共筛选了3844篇文章，其中18篇被选中。所选研究发表于2018年至2023年之间，研究设计各不相同，在10个国家进行。确定了几个障碍和促进因素，并分为四个领域：医疗保健系统、医疗保健专业人员、医疗保健用户和信息技术。在医疗保健系统中，临床效率是最常被报道的。关于医疗保健专业人员，教育是最常被讨论的。在医疗保健用户方面，研究最常确定与患者结果相关的因素，而在信息技术领域，诊断性能是最常被探讨的。
随着人工智能用于糖尿病视网膜病变筛查疗效的证据不断增加，其在临床实践中的应用障碍和促进因素是至关重要的考虑因素。将系统、提供者、消费者和技术因素的知识转化为临床策略，最终有助于这项新技术在筛查实践中的可持续和有效实施。
