Integrating artificial intelligence into medical device quality systems and health-care practice

Quality Control (QC) and Quality Assurance (QA) are central to safety, effectiveness, and regulatory compliance of medical devices. The transition from 21 CFR 820 to the Quality Management System Regulation (QMSR) formalizes harmonization with ISO 13485. Artificial intelligence (AI) is being explored in clinical and manufacturing settings and may support medical-device quality and compliance when appropriately validated.

This review examines AI applications within QC, QA, and regulatory systems across the product lifecycle. Health-care examples are included only when they illustrate quality, validation, human oversight, or lifecycle-management considerations relevant to medical devices. Clinical and manufacturing applications are discussed in the context of FDA Computer Software Assurance and quality frameworks. A narrative literature search used PubMed, Google Scholar, FDA.gov, EUR-Lex, and IMDRF.org and covered publications from January 2010 through September 2025.

AI can shift medical device QC and QA from reactive approaches toward predictive, risk-based paradigms. When integrated within a robust QMS, AI can improve process capability, reduce human variability, and enable earlier detection of quality and compliance risks. Adoption should be guided by regulatory science, clear intended use, lifecycle validation, effective change control, transparency, and sustained human oversight to protect patient safety and maintain regulatory compliance.