Evidence map›Paper›PMID 42143125›Full record

ReviewNPJ digital medicine2026

Integrating artificial intelligence tools in health research.

Gemma Postill, Jana Sedlakova, Stefano Tancredi, Frerik Smit, Sintieh Ekongefeyin, Andreas M Baumer, Arnaud Chiolero, Jürgen Bernard, Laura C Rosella, Viktor von Wyl

Erratum issuedAbstract 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. An erratum has been issued. 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

5 · Who and what money

Authors and funding

10 authors.

Gemma Postill *Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada. gemma.postill@utoronto.ca.
Jana Sedlakova *Department of Informatics, University of Zurich, Zürich, Switzerland.
Stefano TancrediPopulation Health Laboratory (#PopHealthLab), University of Fribourg, Fribourg, Switzerland.
Frerik SmitPopulation Health Laboratory (#PopHealthLab), University of Fribourg, Fribourg, Switzerland.
Sintieh EkongefeyinDigital Society Initiative, University of Zurich, Zürich, Switzerland.
Andreas M BaumerInstitute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland.
Arnaud ChioleroPopulation Health Laboratory (#PopHealthLab), University of Fribourg, Fribourg, Switzerland.
Jürgen BernardTemerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Laura C RosellaDalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada. laura.rosella@utoronto.ca.
Viktor von WylSwiss School of Public Health (SSPH+), Zurich, Switzerland. viktor.vonwyl@uzh.ch.

Funding

Universität Zürich Global Seed Fund grant
6 · The paper itself

Abstract

Disciplines conducting health research rely on discipline-specific workflows that are distinct from data science, the discipline which develops most artificial intelligence (AI)-enabled research tools. Here we reflect on the impact of such differences in workflows and provide practical advice for leveraging AI-enabled research tools in health research. Further, we provide recommendations for how to prepare the next generation of health researchers for the inevitable influx of research integrating AI-enabled tools.

Identifiers

PMID42143125
PMCPMC13438755

What OpenQuestion 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 OpenQuestion graph.