AI Medical Device Safety: Scoping Review with ☸️SAIMSARA.

Name: SAIMSARA Evidence Object digital::AI_MEDICAL_DEVICE_SAFETY_SS
Creator: SAIMSARA
License: https://saimsara.com/license/

SAIMSARA

doi:10.62487/saimsaraa33bf709

SAIMSARA Journal

Machine-Readable Science • ISSN 3054-3991

AI Medical Device Safety: Scoping Review with ☸️SAIMSARA.

Digital Health & Biotech

Issue 3, Volume 1, 2026

Chat with this issue

DOI: 10.62487/saimsaraa33bf709

Editorial note

• Last update: 2026-05-18 13:01:10

What is this paper about

AI medical device safety is no longer just about model accuracy — this evidence map shows why real risk emerges across the full lifecycle: retraining, workflow integration, postmarket surveillance, cybersecurity, and human oversight. Built from 146 references and 151 original studies, the full map gives a practical view of where AI medical devices are already improving safety, where reporting and regulation still fail, and which safeguards matter before deployment.

Human-verified editorial review Verified by World ID proof-of-human. This editorial layer was submitted from a SAIMSARA account verified as a unique human.

Abstract: To synthesize the available structured evidence on AI medical device safety, with emphasis on lifecycle risk management, postmarket surveillance, clinical deployment, cybersecurity, usability, workflow integration, and regulatory oversight. The review uses 146 references and builds its evidence map from 151 original studies with 1823692 total participants/sample observations (topic-deduplicated ΣN). Across the mapped evidence, AI medical device safety emerges predominantly as a lifecycle governance challenge rather than a one-time performance question, with risks shifting after deployment through model updates, workflow integration, and connected infrastructure. Recurrent signals indicate that postmarket reporting is often insufficient to attribute harms to AI components, with 34.5% of FDA medical device reports lacking adequate AI/ML information and less than 2% of approved AI devices updated through retraining despite documented site-specific performance shifts. Promising mitigations span deployment safety cases, usability engineering, threshold customization, and IoMT cybersecurity monitoring, but their clinical impact remains supported mainly by experimental and early-phase evidence. This suggests a practical role for adaptive, auditable oversight frameworks that link premarket clearance to continuous real-world monitoring. Future research should prioritize prospective deployment studies with standardized AI-specific safety reporting to close the gap between technical promise and demonstrated patient benefit.

Keywords: AI medical devices; Medical device safety; Post-market surveillance; Adverse event reporting; Regulatory compliance; Software as medical device; Model retraining; Distribution shift; Predictive maintenance; Clinical workflow safety

Review Stats

Final search date and database lock: 2026-05-14 22:21:05 CEST
Plan: Pro (expanded craft tokens; source: Semantic Scholar)
Source: Semantic Scholar
Total Abstracts/Papers: 707
Downloaded Abstracts/Papers: 707
Included original and non-original Abstracts/Papers (all): 295
Included original Abstracts/Papers (Vote counting by direction of effect): 151
Reference Index (links used in paper): 146
Total participants/sample observations (topic deduplicated ΣN): 1823692

Get access to the full paper

Unlock the full evidence map

Full paper access includes the complete human-readable review, figures, reference index, PDF export, and machine-readable Evidence JSON download.
Evidence JSON can also be purchased separately if you only need the LLM-ready object for agent, AI, or RAG workflows.
Institutional or library access? Sign in with your institution email to open all available SAIMSARA papers under your institution access arrangement.
Need a SAIMSARA review on your own topic? ☸️Request.

Reference Index (146)

[1] Regulating AI Adaptation: An Analysis of AI Medical Device Updates — https://doi.org/10.1101/2024.06.26.24309506
[2] Medical Device Safety: An Analytical International Survey of AI/ML-Enabled Medical Devices — https://doi.org/10.1109/cai64502.2025.00108
[3] AI-Enhanced Predictive Analytics for Quality System Regulation (QSR) Compliance and Defect Minimization in Medical Device Production — https://doi.org/10.1109/icat2i69744.2025.11472723
[4] Feasibility of Using LLMs to Automate Analysis of AI/ML Medical Device Approvals — https://doi.org/10.1145/3774816.3774830
[5] AI-Enhanced Medical Device Software Testing: Improving Efficiency and Coverage — https://doi.org/10.47363/pms/2024(8)e105
[6] Artificial intelligence related safety issues associated with FDA medical device reports — https://doi.org/10.1038/s41746-024-01357-5
[7] AI as a Medical Device Adverse Event Reporting in Regulatory Databases: Protocol for a Systematic Review — https://doi.org/10.2196/48156
[8] Real-Time Edge AI with Explainable Deep Learning for Predictive Maintenance of Medical Devices: *Instantaneous Detection of Medical Device Failures using Edge AI — https://doi.org/10.1109/isiber68248.2026.11470555

Unlock the full paper to view the complete Reference Index.