Barriers and Enablers Influencing the Implementation of Artificial Intelligence for Diabetic Retinopathy Screening in Clinical Practice: A Scoping Review.

IF 4.9 2区 医学 Q1 OPHTHALMOLOGY
Jenny Tran, Jose J Estevez, Natasha J Howard, Saravana Kumar
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引用次数: 0

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

影响人工智能在糖尿病视网膜病变临床筛查中的应用的障碍和促进因素:范围综述。
背景:糖尿病视网膜病变是世界范围内可预防性失明的主要原因。与此同时,人工智能在医学领域的临床应用也在迅速增长。本综述旨在识别和总结人工智能系统用于糖尿病视网膜病变筛查的临床应用的障碍和促进因素的现有文献。方法:采用系统方法和prism - scr协议进行范围评价,在MEDLINE、Embase、Emcare、Cochrane、CINAHL、ProQuest、Scopus和灰色文献(Australian Indigenous Health InfoNet)中进行检索。两名审稿人独立审查了这些记录。第三位审稿人提供了共识。随后以叙述形式进行数据提取和综合。结果:共筛选3844篇,入选18篇。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。确定了几个障碍和推动因素,并将其分为四个领域: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。关于保健专业人员,最常讨论的是教育。在医疗保健用户中,研究最常确定与患者结果相关的因素,而诊断性能最常在信息技术领域进行探索。结论:随着人工智能对糖尿病视网膜病变筛查有效性的证据越来越多,其在临床实践中应用的障碍和推动因素是最重要的考虑因素。将系统、提供者、消费者和技术因素的知识转化为临床策略,最终促进这种新技术在筛查实践中的可持续和有效实施。
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来源期刊
CiteScore
7.60
自引率
12.50%
发文量
150
审稿时长
4-8 weeks
期刊介绍: Clinical & Experimental Ophthalmology is the official journal of The Royal Australian and New Zealand College of Ophthalmologists. The journal publishes peer-reviewed original research and reviews dealing with all aspects of clinical practice and research which are international in scope and application. CEO recognises the importance of collaborative research and welcomes papers that have a direct influence on ophthalmic practice but are not unique to ophthalmology.
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