Evidence map›Paper›PMID 42781556›Full record

ReviewBMJ digital health & AI2026

When hospitals build their own AI: a comparison of the EU and US regulatory framework.

Sofia Palmieri, Timo Minssen, I Glenn Cohen

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

3 authors.

Sofia PalmieriHarvard Law School, Cambridge, Massachusetts, USA.ORCID https://orcid.org/0000-0002-6618-5963
Timo MinssenCentre for Advanced Studies in Bioscience Innovation Law (CeBIL), University of Copenhagen, Copenhagen, Capital Region of Denmark, Denmark.
I Glenn CohenHarvard Law School, Cambridge, Massachusetts, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hospitals are increasingly shifting from being passive purchasers of artificial intelligence (AI) systems to active developers of medical AI, particularly in the form of in-hospital machine learning tools tailored to local clinical needs. This paper examines how the European Union (EU) and the USA regulate in-hospital-developed AI-based medical devices, focusing on the legal and institutional conditions that shape innovation, oversight and diffusion. Through a comparative analysis across three dimensions-responsiveness to clinical need, distribution of innovative capacity and pathways for scaling and diffusion-the paper shows that both systems enable hospital-based innovation but embed it in different regulatory logics. The EU adopts a structured model that treats hospitals as regulated innovators operating under defined institutional obligations, whereas the USA relies on a more permissive and less institutionally differentiated framework in which hospital innovation emerges as a by-product of general software regulation.

Indexed as

Artificial intelligenceHospitalsSoftware

Identifiers

PMID42781556
PMCPMC13599951

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.