Total product lifecycle regulatory considerations and recommendations for generative AI-enabled medical devices
Open accessAbstract
As generative artificial intelligence (GenAI) emerges within the healthcare ecosystem, the regulatory environment surrounding these technologies remains fragmented. Importantly, GenAI in healthcare requires adapting the established Total Product Lifecycle (TPLC) paradigm to non-deterministic, rapidly evolving software, translating it into concrete steps for pre-market evaluation, change controls, and post-market performance monitoring to keep devices safe and effective as they change. This manuscript elucidates the need for developers, healthcare providers, and clinicians to engage proactively with regulatory compliance during the development and deployment phases of genAI-enabled medical devices. By understanding the nuances of national and international regulations, stakeholders can better navigate the unique risks of the evolving landscape of GenAI-enabled medical devices, while ensuring patient safety and outcome optimisation. Finally, this manuscript highlights the importance of the TPLC approach in managing the risks associated with GenAI-enabled technologies, offering recommendations for improving health outcomes and balancing the interests of various stakeholders.
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The FDA has adopted a cautious stance toward generative AI in regulated clinical settings, emphasizing validation requirements, predetermined change control plans, and continuous post-market performance monitoring.
“The FDA has adopted a cautious stance toward generative AI in regulated clinical settings, emphasizing validation requirements, predetermined change control plans, and continuous post-market performance monitoring (Ong et al., 2026; Armoundas and Singh, 2026).”
From A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification (2026) · cites this paper as Armoundas and Singh, 2026
The Pre-market evaluation section says the FDA has identified challenges in evaluating the safety and effectiveness of GenAI-enabled devices, and the Risks and risk management section lists FDA guidance on predetermined change control plans. The Post-market monitoring section calls for continuous performance monitoring, but presents it as a needed strategy rather than an FDA requirement already adopted for GenAI. Thus the paper supports the cautious regulatory approach, but not the claim that the FDA has adopted all three measures as requirements in regulated clinical settings. (LLM, conf=0.86)
Experimental — accuracy not yet measuredRegulatory and reporting guidance increasingly acknowledges the need for lifecycle-based evaluation of AI systems.
“Regulatory and reporting guidance increasingly acknowledges the need for lifecycle-based evaluation of AI systems [21–23].”
From Validation is not enough: Longitudinal evidence of post-deployment fragility in clinical AI systems (2026) · cites this paper as [21]
The Risks and risk management section describes EU AI Act requirements for living documentation, lifecycle logging, post-market monitoring, and robustness assessment throughout the lifecycle. This supports the regulatory part of the claim, but the paper does not establish that reporting guidance increasingly acknowledges lifecycle-based evaluation. (LLM, conf=0.78)
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Self-citation
- Authors
- Armoundas, A. A., & Singh, J. P.
- Affiliations
- Broad Institute (US)
- Massachusetts General Hospital (US)
- Corresponding author
- Armoundas, Antonis A
- Venue
- European Heart Journal - Digital Health
- Publisher
- Oxford University Press (OUP)
- Published
- 2026-02-03
- Volume
- 7
- Issue
- 3
- Pages
- ztag019-ztag019
- ISSN
- 2634-3916 (ISSN-L)
- PMC
- PMC12959241
- OpenAlex
- W7127351568
- Cited by
- 8
- References
- 21
- Citations by year
- 2026: 8
- FWCI
- 60.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- European Heart Journal - Digital Health (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Funders
- Institute of Precision Medicine (17UNPG33840017)
- American Heart Association
- RICBAC Foundation
- NIH (1 R01 HL135335-01, 1 R01 HL161008-01, 1 R21 HL137870-01, 1 R21EB026164-01, 3R21EB026164-02S1)
- Topics
- Health Sciences › Health Professions › Health Information Management › Healthcare Regulation
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- generative AI, medical devices, regulatory compliance, change control, post-market monitoring, AI risk management, patient safety, health care regulation
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