Regulations/FDA QMSR/AI Governance
AI Governance · FDA QMSR

AI Governance for FDA QMSR.

This page focuses on AI governance implications inside regulated work. It shows where AI changes evidence needs while keeping quality, regulatory, data, supplier, and human accountability controls in view.

Source basis: Quality Management System Regulation amendments to 21 CFR Part 820Use: evidence-readinessBoundary: not legal advice
FDA QMSR TRACE 21 CFR PART 820 SCQMSR FINAL RULE ISO 13485:2016 IFDA-SPECIFIC PRO
/ AI governance

AI changes the evidence pattern, not the need for control.

FDA QMSR
Control 01

AI in QMS tools

AI-enabled eQMS, complaint triage, CAPA assistance, or supplier analytics should be controlled as quality-system software where applicable.

Control 02

Design controls

AI-enabled device products still need design and risk evidence under device QMS expectations.

Control 03

Supplier AI

Cloud and AI vendors should appear in supplier controls and quality agreements where they influence regulated work.

Control 04

Management review

AI-related quality metrics should be visible when they affect the QMS.

/ Adjacent controls

AI governance must connect to existing regulated systems.

not isolated
Evidence 01

QMSR transition plan

Connect this evidence to QMS, clinical, software, supplier, data, or lifecycle governance where applicable.

Evidence 02

ISO 13485 gap assessment

Connect this evidence to QMS, clinical, software, supplier, data, or lifecycle governance where applicable.

Evidence 03

Procedure update register

Connect this evidence to QMS, clinical, software, supplier, data, or lifecycle governance where applicable.

Evidence 04

Training completion record

Connect this evidence to QMS, clinical, software, supplier, data, or lifecycle governance where applicable.

Evidence 05

Design control evidence index

Connect this evidence to QMS, clinical, software, supplier, data, or lifecycle governance where applicable.

Evidence 06

Risk management file crosswalk

Connect this evidence to QMS, clinical, software, supplier, data, or lifecycle governance where applicable.