ReviewBMJ digital health & AI2026
When are large language models medical devices? Navigating the MDR and AI Act for healthcare applications.
Review in BMJ digital health & AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The flexibility of generative artificial intelligence (AI), and specifically large language models (LLMs), holds promise to transform healthcare by offering new tools to improve patient care and enhance clinician efficiency. However, this flexibility introduces new challenges, as LLM applications might inadvertently be used in ways that do not align with the intended purpose of the manufacturer, raising patient safety concerns and liability issues. We have identified a regulatory gap whereby LLM applications that could be repurposed, malfunction or be inadvertently misused for medical purposes fall outside the scope of the Medical Device Regulation (MDR) because they lack a medical intended purpose, and outside meaningful AI Act safety obligations because they do not qualify as high-risk systems, yet still present patient safety risks without adequate regulatory guidance. In this manuscript, we aim to address this regulatory gap for LLM applications in healthcare by identifying these ambiguous grey-area applications and providing four recommendations-validation, mitigation, compliance and alignment-focused on prevention and risk mitigation rather than reclassification as medical devices. These recommendations are intended to advance the responsible implementation of LLM applications in healthcare while ensuring patient safety.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.