Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, CH-3010 Bern, Switzerland.
Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, CH-3010 Bern, Switzerland.
Z Med Phys. 2024 May;34(2):343-352. doi: 10.1016/j.zemedi.2024.02.001. Epub 2024 Feb 27.
The use of artificial intelligence systems in clinical routine is still hampered by the necessity of a medical device certification and/or by the difficulty of implementing these systems in a clinic's quality management system. In this context, the key questions for a user are how to ensure robust model predictions and how to appraise the quality of a model's results on a regular basis. In this paper we discuss some conceptual foundation for a clinical implementation of a machine learning system and argue that both vendors and users should take certain responsibilities, as is already common practice for high-risk medical equipment. We propose the methodology from AAPM Task Group 100 report No. 283 as a conceptual framework for developing risk-driven a quality management program for a clinical process that encompasses a machine learning system. This is illustrated with an example of a clinical workflow. Our analysis shows how the risk evaluation in this framework can accommodate artificial intelligence based systems independently of their robustness evaluation or the user's in-house expertise. In particular, we highlight how the degree of interpretability of a machine learning system can be systematically accounted for within the risk evaluation and in the development of a quality management system.
人工智能系统在临床常规中的应用仍然受到医疗器械认证的必要性以及在临床质量管理体系中实施这些系统的困难的阻碍。在这种情况下，用户的关键问题是如何确保稳健的模型预测，以及如何定期评估模型结果的质量。在本文中，我们讨论了将机器学习系统临床实施的一些概念基础，并认为供应商和用户都应该承担一定的责任，就像高风险医疗设备已经实行的那样。我们提出了 AAPM 工作组 100 报告第 283 号的方法作为一个概念框架，用于为包含机器学习系统的临床流程开发风险驱动的质量管理计划。这通过一个临床工作流程的示例来说明。我们的分析表明，在这个框架中，风险评估如何可以独立于其稳健性评估或用户的内部专业知识来容纳基于人工智能的系统。特别是，我们强调了如何在风险评估和质量管理系统的开发中系统地考虑机器学习系统的可解释性程度。
