Evidence map›Paper›PMID 41673095›Full record

ReviewNPJ digital medicine2026

Current challenges and the way forwards for regulatory databases of artificial intelligence as a medical device.

Ariel Yuhan Ong, Aditya U Kale, Joe Antoun, Henry David Jeffry Hogg, Ben Hammond, Pearse A Keane, Russell Pearson, Hugh Harvey, Alastair K Denniston

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Ariel Yuhan OngMoorfields Eye Hospital NHS Foundation Trust, London, United Kingdom. ariel.ong@nhs.net.
Aditya U KaleUniversity Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.
Joe AntounHardian Health, Haywards Heath, United Kingdom.
Henry David Jeffry HoggUniversity Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.
Ben HammondCollege of Medicine and Health, University of Birmingham, Birmingham, United Kingdom.
Pearse A KeaneMoorfields Eye Hospital NHS Foundation Trust, London, United Kingdom.
Russell PearsonMedicines and Healthcare Products Regulatory Agency, London, United Kingdom.
Hugh HarveyCollege of Medicine and Health, University of Birmingham, Birmingham, United Kingdom.
Alastair K DennistonNIHR Moorfields Biomedical Research Centre, London, United Kingdom. a.denniston@bham.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Effective regulatory oversight is a key step in ensuring that artificial intelligence as a medical device (AIaMD) is safe in real-world clinical settings. In this Perspective, we provide insights from our experience working with international regulatory databases, informed by our recent research and the expertise of the multidisciplinary authorship team. We highlight four key challenges, discuss attempts to circumvent these limitations, and highlight emerging initiatives. Nevertheless, the underlying issue of the quality and availability of input data from regulatory databases remains. We discuss considerations for improving accessibility and transparency, outline key aspects for a next-generation regulatory data ecosystem for AIaMDs, and call on global stakeholders to come together and align efforts to develop a clear roadmap to accelerate safe innovation and improve outcomes for patients worldwide.

Identifiers

PMID41673095
PMCPMC13004881

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.