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.

Download PDF

• QMS as a framework for health AI
• Establishing a proactive culture of quality
• Establishing risk-based design, development, and monitoring
• Establishing a compliance-facilitating infrastructure
• Conclusion
• References
• Acknowledgements
• Author information
• Ethics declarations
• Additional information
• Rights and permissions
• About this article