Mercolli, Lorenzo; Rominger, Axel Oliver; Shi, Kuangyu (2024). Towards quality management of artificial intelligence systems for medical applications. (In Press). Zeitschrift für medizinische Physik Elsevier 10.1016/j.zemedi.2024.02.001
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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.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Clinic of Nuclear Medicine |
UniBE Contributor: |
Mercolli, Lorenzo, Rominger, Axel Oliver, Shi, Kuangyu |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
1876-4436 |
Publisher: |
Elsevier |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
28 Feb 2024 14:00 |
Last Modified: |
29 Feb 2024 02:48 |
Publisher DOI: |
10.1016/j.zemedi.2024.02.001 |
PubMed ID: |
38413355 |
Uncontrolled Keywords: |
Artificial intelligence Machine learning Quality management Risk analysis |
BORIS DOI: |
10.48350/193540 |
URI: |
https://boris.unibe.ch/id/eprint/193540 |
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