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TRUVACE RECORD VERSION
record: TRV-2026-0771
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-15T06:22:10.921687Z
status: published
lens: trace
sector: policy
headline: Reframing risk management for AI-enabled medical devices: A dual-layer risk governance framework
dek: BackgroundAI-enabled medical devices introduce dynamic, data-dependent risks that challenge traditional safety-risk management frameworks. While ISO 14971, AAMI CR34971, and the EU Artificial Intelligence Act each address elements of device safety and algorithmic governance, they remain fragmented when applied. This review examines conceptual and operational gaps in current approaches and proposes an integrated governance model for AI-specific safety-risk management.MethodsA structured narrative review was condu…
gain_title: The review proposes an integrated dual-layer governance model that aligns AI-specific risk identification with ISO 14971 processes and EU AI Act obligations, giving regulators and manufacturers a clearer actionable pathway for lifecycle monitoring.
problem_title: AI-enabled medical devices create dynamic, data-dependent hazards that current ISO 14971, AAMI CR34971 and EU AI Act approaches address only in fragmented form, leaving gaps in hazard linkage, control adequacy, and lifecycle monitoring.
trace_subject: safety-risk governance for AI-enabled medical devices linking AI-specific hazards to ISO 14971 and EU AI Act lifecycle requirements
gain_reading: The review proposes an integrated dual-layer governance model that aligns AI-specific risk identification with ISO 14971 processes and EU AI Act obligations, giving regulators and manufacturers a clearer actionable pathway for lifecycle monitoring.
gain_evidence: An integrated governance model is proposed to align AI-specific risk identification with established medical-device safety frameworks. | This synthesis provides regulators, manufacturers, and professionals with a clearer, more actionable approach to managing AI-related safety risks.
problem_reading: AI-enabled medical devices create dynamic, data-dependent hazards that current ISO 14971, AAMI CR34971 and EU AI Act approaches address only in fragmented form, leaving gaps in hazard linkage, control adequacy, and lifecycle monitoring.
problem_evidence: AI-enabled medical devices introduce dynamic, data-dependent risks that challenge traditional safety-risk management frameworks. | The review identified persistent challenges in linking AI-specific hazards to safety-risk evaluation, determining adequacy of risk controls, integrating algorithmic-risk obligations with ISO 14971 processes, and operationalizing lifecycle monitoring under the EU AI Act.
quick_read: A peer-reviewed review published August 13, 2026 examined how AI-enabled medical devices challenge traditional safety-risk management. Drawing on 19 academic and regulatory sources, it found ISO 14971, AAMI CR34971 and the EU AI Act each cover parts of device safety and algorithmic governance but remain fragmented in practice.

The fragmentation matters because dynamic, data-dependent AI hazards are not consistently linked to safety-risk evaluation or control adequacy, complicating lifecycle oversight for regulators and manufacturers. The authors propose an integrated dual-layer governance model to align AI-specific identification with established device safety processes, though the paper presents a conceptual synthesis rather than empirical validation of the model in deployed devices.
limitation: 
tag: Dual reading
key_points: Narrative review of 19 academic and regulatory sources from PubMed, IEEE Xplore, Google Scholar and regulatory repositories. | Identified gaps in linking AI-specific hazards to safety-risk evaluation and determining adequacy of risk controls. | Analyzed fragmentation between ISO 14971, AAMI CR34971, and EU Artificial Intelligence Act for AI-enabled devices. | Proposed integrated model to operationalize lifecycle monitoring and algorithmic-risk obligations within device safety processes.
rundown: The authors conducted a structured narrative review using JBI, AACODS and normative appraisal categories, charting 19 eligible studies and regulatory sources focused on AI-specific safety-risk management and risk-analysis methodologies.

Results highlight four persistent operational gaps: linking AI hazards to safety evaluation, judging adequacy of controls, integrating algorithmic-risk obligations with ISO 14971, and implementing lifecycle monitoring required under the EU AI Act.
sources:
- peer_reviewed | International Journal of Risk & Safety in Medicine | https://doi.org/10.1177/09246479261477226 | 2026-08-13
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