Evidence map›Paper›PMID 42469355›Full record

ReviewNPJ digital medicine2026

Artificial intelligence needs better health systems to reduce inequalities.

Robin van Kessel, Alexander W Carter, Khurram Nasir, Elias Mossialos

Abstract readReview
PubMed Full text at PMC Publisher
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. 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

4 authors.

Robin van KesselAI and Digital Lab, LSE Health, Department of Health Policy, London School of Economics and Political Science, London, UK. r.van-kessel@lse.ac.uk.
Alexander W CarterAI and Digital Lab, LSE Health, Department of Health Policy, London School of Economics and Political Science, London, UK.
Khurram NasirAI and Digital Lab, LSE Health, Department of Health Policy, London School of Economics and Political Science, London, UK.
Elias MossialosAI and Digital Lab, LSE Health, Department of Health Policy, London School of Economics and Political Science, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) technologies are increasingly prevalent in healthcare, yet without adaptive governance, even well-designed systems risk exacerbating health inequalities. This perspective examines five interconnected governance domains: legal frameworks, evidence generation, regulation and market access, workforce readiness, and public trust. We argue these form a cyclical governance chain in which weaknesses cascade across domains, and identify critical gaps and system-level reforms to ensure AI reduces rather than amplifies disparities.

Identifiers

PMID42469355
PMCPMC13639021

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.