A Perspective on a Quality Management System for AI/ML-Based Clinical Decision Support in Hospital Care.
AI
ISO15189
clinical decision support
implementation
machine learning (ML)
quality management system
Journal
Frontiers in digital health
ISSN: 2673-253X
Titre abrégé: Front Digit Health
Pays: Switzerland
ID NLM: 101771889
Informations de publication
Date de publication:
2022
2022
Historique:
received:
12
05
2022
accepted:
16
06
2022
entrez:
25
7
2022
pubmed:
26
7
2022
medline:
26
7
2022
Statut:
epublish
Résumé
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
Identifiants
pubmed: 35873347
doi: 10.3389/fdgth.2022.942588
pmc: PMC9299425
doi:
Types de publication
Journal Article
Langues
eng
Pagination
942588Informations de copyright
Copyright © 2022 Bartels, Dudink, Haitjema, Oberski and van ‘t Veen.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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