Bartels Richard, Dudink Jeroen, Haitjema Saskia, Oberski Daniel, van 't Veen Annemarie
Digital Health, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Department of Neonatology, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Front Digit Health. 2022 Jul 6;4:942588. doi: 10.3389/fdgth.2022.942588. eCollection 2022.
Although many artificial intelligence (AI) and machine learning (ML) based algorithms are being developed by researchers, only a small fraction has been implemented in clinical-decision support (CDS) systems for clinical care. Healthcare organizations experience significant barriers implementing AI/ML models for diagnostic, prognostic, and monitoring purposes. In this perspective, we delve into the numerous and diverse quality control measures and responsibilities that emerge when moving from AI/ML-model development in a research environment to deployment in clinical care. The Sleep-Well Baby project, a ML-based monitoring system, currently being tested at the neonatal intensive care unit of the University Medical Center Utrecht, serves as a use-case illustrating our personal learning journey in this field. We argue that, in addition to quality assurance measures taken by the manufacturer, user responsibilities should be embedded in a quality management system (QMS) that is focused on life-cycle management of AI/ML-CDS models in a medical routine care environment. Furthermore, we highlight the strong similarities between AI/ML-CDS models and diagnostic devices and propose to use ISO15189, the quality guideline for medical laboratories, as inspiration when building a QMS for AI/ML-CDS usage in the clinic. We finally envision a future in which healthcare institutions run or have access to a medical AI-lab that provides the necessary expertise and quality assurance for AI/ML-CDS implementation and applies a QMS that mimics the ISO15189 used in medical laboratories.
尽管研究人员正在开发许多基于人工智能（AI）和机器学习（ML）的算法，但只有一小部分已在临床决策支持（CDS）系统中用于临床护理。医疗保健组织在为诊断、预后和监测目的实施AI/ML模型时遇到了重大障碍。从这个角度来看，我们深入探讨了从研究环境中的AI/ML模型开发到临床护理部署过程中出现的众多不同的质量控制措施和责任。“睡个好觉宝宝”项目是一个基于ML的监测系统，目前正在乌得勒支大学医学中心的新生儿重症监护病房进行测试，它作为一个用例说明了我们在该领域的个人学习历程。我们认为，除了制造商采取的质量保证措施外，用户责任应纳入质量管理体系（QMS），该体系专注于医疗常规护理环境中AI/ML-CDS模型的生命周期管理。此外，我们强调了AI/ML-CDS模型与诊断设备之间的强烈相似性，并建议在构建临床使用AI/ML-CDS的QMS时，以医学实验室的质量指南ISO15189为灵感。我们最终设想了一个未来，医疗保健机构运营或可以使用一个医学AI实验室，该实验室为AI/ML-CDS的实施提供必要的专业知识和质量保证，并应用一个模仿医学实验室使用的ISO15189的QMS。
