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Reframing risk management for AI-enabled medical devices: A dual-layer risk governance framework
1Quality Engineering, Becton Dickinson and Company, San Diego, California, USA.
The International Journal of Risk & Safety in Medicine
|August 13, 2026
Summary
AI medical device safety requires integrated risk management. Current frameworks are fragmented, necessitating a unified governance model for AI-specific hazards and regulatory compliance.
Area of Science:
- Medical Device Safety
- Artificial Intelligence in Healthcare
- Regulatory Science
Background:
- AI-enabled medical devices present unique, data-dependent risks.
- Existing safety frameworks (ISO 14971, AAMI CR34971, EU AI Act) are fragmented for AI governance.
- A gap exists in unified approaches to AI-specific safety-risk management.
Purpose of the Study:
- To examine conceptual and operational gaps in current AI safety-risk management.
- To propose an integrated governance model for AI-specific safety-risk management.
- To enhance the safety and governance of AI-enabled medical devices.
Main Methods:
- Structured narrative review of literature and regulatory documents.
- Searches conducted in PubMed, IEEE Xplore, Google Scholar, and regulatory repositories.
- Synthesis of 19 studies and regulatory sources using JBI, AACODS, and normative appraisal.
Main Results:
- Identified challenges in linking AI hazards to risk evaluation and control adequacy.
- Highlighted difficulties in integrating AI risk obligations with ISO 14971.
- Found existing frameworks address AI risk components but lack a unified operational pathway.
Conclusions:
- An integrated governance model is proposed to align AI risk identification with medical device safety frameworks.
- This model offers a clearer, actionable approach for managing AI-related safety risks.
- Aims to support regulators, manufacturers, and professionals in AI medical device safety.
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