自主人工智能在糖尿病视网膜病变检测中的应用——医疗系统成功应用的经验教训

IF 4.6 Q1 OPHTHALMOLOGY

Ophthalmology science Pub Date : 2025-09-03 DOI:10.1016/j.xops.2025.100935

Clare W. Teng MD , Saawan D. Patel BS , Andrew J. Barkmeier MD , T.Y. Alvin Liu MD , David Myung MD, PhD , Jeffrey Henderer MD , James Liu MD , Eric Hansen MD , Lama A. Al-Aswad MD, MPH

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引用次数: 0

摘要

人工智能（AI）辅助糖尿病视网膜病变（DR）检测系统已经商业化5年，但采用仍然相对有限。本文旨在总结临床环境中的证据，描述目前的采用状态，并分享成功实施的主题。诊断测试或技术的设计评估参与者眼科医生方法我们进行了文献综述，并与几个学术卫生系统中领导实施人工智能辅助DR测试项目的眼科医生进行了访谈。该研究的重点是目前美国食品和药物管理局批准的3种人工智能系统：LumineticsCore、EyeArt和AEYE诊断筛查（AEYE- ds），评估了它们的性能和卫生系统用于在诊所有效实施该技术的策略。主要观察指标：诊断准确性数据，眼科医生反馈。结果文献综述发现6份报告了初级保健办公室设置中自主AI DR测试的诊断准确性数据的出版物，包括5份LumineticsCore和1份EyeArt。其他文章（其中18篇被选中进行详细审查）讨论了对患者依从性、卫生公平和碳足迹的影响，以及成本效益和工作流程效率分析。没有研究对同一病人的系统进行比较。总的来说，人工智能系统的采用者报告的平均非骨髓分级率为49%至75% (n = 5)，灵敏度为87%至100% (n = 3)，特异性为60%至91% （n = 4）。根据撰写本文时的公开记录，LumineticsCore和EyeArt在美国都有5个学术采采者。鉴于最近的监管许可，关于AEYE-DS的信息有限。成功实施的要素包括适当的地点选择，将人工智能工具与初级保健诊所工作流程相结合，简化患者参与和转诊，以及对工作人员的持续培训。利用人工智能的卫生系统报告了改善的卫生保健有效性数据和信息集措施、卫生公平、生产力和患者对眼科随访的依从性。结论人工智能辅助糖尿病眼科检查是一种很有前景的解决方案，有助于早期发现DR，促进公平获取，并降低系统层面的护理成本。它的成功实施需要解决技术、操作和涉众参与方面的挑战。我们的研究强调，只要对人工智能的采用进行战略管理，人工智能就有可能彻底改变医疗服务。财务披露作者在本文中讨论的任何材料中没有/作者没有专有或商业利益。

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Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption

Purpose

Artificial intelligence (AI)–aided diabetic retinopathy (DR) testing systems have been commercialized for 5 years, but adoption is still relatively limited. This article aims to summarize the evidence in clinical settings, describe the current state of adoption, and share themes of successful implementation.

Design

Evaluation of diagnostic test or technology.

Participants

Ophthalmologists.

Methods

We performed literature review and conducted interviews with ophthalmologists leading implementation of AI-aided DR testing programs at several academic health systems. The study focused on the 3 currently US Food and Drug Administration-cleared AI systems: LumineticsCore, EyeArt, and AEYE Diagnostic Screening (AEYE-DS), assessing their performance and strategies utilized by health systems to effectively implement this technology in clinics.

Main Outcome Measures

Diagnostic accuracy data, ophthalmologist feedback.

Results

The literature review found 6 publications reporting diagnostic accuracy data of autonomous AI DR testing in primary care office settings, including 5 for LumineticsCore and 1 for EyeArt. Additional articles, of which 18 were selected for detailed review, addressed impact on patient adherence, health equity, and carbon footprint, as well as cost-effectiveness and workflow efficiency analyses. There were no studies comparing the systems on the same patients. In aggregate, adopters of the AI systems reported average nonmydriatic gradability of 49% to 75% (n = 5), sensitivity 87% to 100% (n = 3), and specificity 60% to 91% (n = 4). Based on public records at the time of writing, both LumineticsCore and EyeArt have >5 academic adopters in the United States. Limited information is available on AEYE-DS given recency of regulatory clearance. Elements of successful implementation include proper site selection, aligning AI tools with primary care clinic workflows, streamlining patient engagement and referrals, and ongoing training of staff. Health systems utilizing AI reported improved Healthcare Effectiveness Data and Information Set measures, health equity, productivity, and patient adherence to follow-up with ophthalmology.

Conclusions

Artificial intelligence–aided diabetic eye examinations present a promising solution to facilitate early detection of DR, promote equitable access, and drive down system-level cost of care. Its successful implementation requires addressing technological, operational, and stakeholder engagement challenges. Our study underscores the potential of AI to revolutionize care delivery provided its adoption is strategically managed.