Evidence map›Paper›PMID 41857339›Full record

ReviewNPJ digital medicine2026

Innovating global regulatory frameworks for generative AI in medical devices is an urgent priority.

Jasmine Chiat Ling Ong, Yilin Ning, Mingxuan Liu, Yian Ma, Liang Zhao, Kuldev Singh, Robert T Chang, Silke Vogel, John C W Lim, Iris Siu Kwan Tan and 6 more

Abstract readReview
In one paragraph

Review in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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

16 authors.

Jasmine Chiat Ling OngDivision of Pharmacy, Singapore General Hospital, Singapore, Singapore.
Yilin NingDuke-NUS AI + Medical Sciences Initiative, Duke-NUS Medical School, Singapore, Singapore.
Mingxuan LiuDuke-NUS AI + Medical Sciences Initiative, Duke-NUS Medical School, Singapore, Singapore.
Yian MaDuke-NUS AI + Medical Sciences Initiative, Duke-NUS Medical School, Singapore, Singapore.
Liang ZhaoDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.
Kuldev SinghStanford University School of Medicine, Stanford, CA, USA.
Robert T ChangStanford University School of Medicine, Stanford, CA, USA.
Silke VogelCentre of Regulatory Excellence, Duke-NUS Medical School, Singapore, Singapore.
John C W LimCentre of Regulatory Excellence, Duke-NUS Medical School, Singapore, Singapore.
Iris Siu Kwan TanArtificial Intelligence Office, Singapore Health Services, Singapore, Singapore.
Oscar FreyerElse Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
Stephen GilbertElse Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
Danielle S BittermanArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
Xiaoxuan LiuCollege of Medicine and Health, University of Birmingham, Birmingham, UK.
Alastair K DennistonCollege of Medicine and Health, University of Birmingham, Birmingham, UK.
Nan LiuDuke-NUS AI + Medical Sciences Initiative, Duke-NUS Medical School, Singapore, Singapore. liu.nan@duke-nus.edu.sg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of generative AI (GenAI) and large language models (LLMs) in healthcare presents both unprecedented opportunities and challenges, necessitating innovative regulatory approaches. In this perspective, we discuss the risks of GenAI and LLM-based medical devices, the limitations of current medical device regulation frameworks when applied to GenAI or LLMs, and advocate for global collaboration in regulatory science research through engaging multidisciplinary expertise and focusing on the needs of diverse populations.

Identifiers

PMID41857339
PMCPMC13168469

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.