Evidence map›Paper›PMID 42851737›Full record

ReviewBMJ digital health & AI2026

When are large language models medical devices? Navigating the MDR and AI Act for healthcare applications.

Jessica D Workum, Sade J Krijgsman, Ildiko Vajda, R M Van Stokkum, Jan van de Brand, Diederik Gommers, Ari Ercole, Michel van Genderen

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jessica D WorkumDepartment of Adult Intensive Care, Erasmus MC, Rotterdam, The Netherlands.ORCID https://orcid.org/0000-0002-4134-0242
Sade J KrijgsmanDepartment of Clinical Physics and Informatics, Maasstad Hospital, Rotterdam, The Netherlands.
Ildiko VajdaDutch Patients' Federation, Utrecht, The Netherlands.
R M Van StokkumDepartment of Risk Analysis for Products in Development, Netherlands Organization for Applied Scientific Research, The Hague, The Netherlands.
Jan van de BrandDepartment of Adult Intensive Care, Erasmus MC, Rotterdam, The Netherlands.
Diederik GommersDepartment of Adult Intensive Care, Erasmus MC, Rotterdam, The Netherlands.
Ari ErcoleDepartment of Medicine, University of Cambridge, Cambridge, UK.ORCID https://orcid.org/0000-0001-8350-8093
Michel van GenderenDepartment of Adult Intensive Care, Erasmus MC, Rotterdam, The Netherlands.ORCID https://orcid.org/0000-0001-5668-3435

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial intelligenceClinical GovernanceMedical Informatics

Identifiers

PMID42851737
PMCPMC13646968

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.