Implementing quality management systems to close the AI translation gap and facilitate safe, ethical, and effective health AI solutions

Autor: Shauna M. Overgaard, Megan G. Graham, Tracey Brereton, Michael J. Pencina, John D. Halamka, David E. Vidal, Nicoleta J. Economou-Zavlanos
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: npj Digital Medicine, Vol 6, Iss 1, Pp 1-5 (2023)
Druh dokumentu: article
ISSN: 2398-6352
36937843
DOI: 10.1038/s41746-023-00968-8
Popis: The integration of Quality Management System (QMS) principles into the life cycle of development, deployment, and utilization of machine learning (ML) and artificial intelligence (AI) technologies within healthcare settings holds the potential to close the AI translation gap by establishing a robust framework that accelerates the safe, ethical, and effective delivery of AI/ML in day-to-day patient care. Healthcare organizations (HCOs) can implement these principles effectively by embracing an enterprise QMS analogous to those in regulated industries. By establishing a QMS explicitly tailored to health AI technologies, HCOs can comply with evolving regulations and minimize redundancy and rework while aligning their internal governance practices with their steadfast commitment to scientific rigor and medical excellence.
Databáze: Directory of Open Access Journals
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