Evidence map›Paper›PMID 42323426›Full record

ArticleNPJ digital medicine2026

Human-AI Collaboration in Healthcare: A Scoping Review.

Joshua Strong, Harry Rogers, Emma Sun, Anna Louise Todsen, Jody Ede, Cherry Lumley, Nick Yeung, Helen Higham, J Alison Noble

Abstract read
In one paragraph

Article 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

9 authors.

Joshua StrongInstitute of Biomedical Engineering, University of Oxford, Oxford, UK. joshua.strong@eng.ox.ac.uk.
Harry RogersInstitute of Biomedical Engineering, University of Oxford, Oxford, UK.
Emma SunInstitute of Biomedical Engineering, University of Oxford, Oxford, UK.
Anna Louise TodsenDepartment of Experimental Psychology, University of Oxford, Oxford, UK.
Jody EdeDepartment of Health and Care Professions, University of Exeter, Exeter, UK.
Cherry LumleyOxford University Hospital NHS Foundation Trust, Oxford, UK.
Nick YeungDepartment of Experimental Psychology, University of Oxford, Oxford, UK.
Helen HighamOxford University Hospital NHS Foundation Trust, Oxford, UK.
J Alison NobleInstitute of Biomedical Engineering, University of Oxford, Oxford, UK.

Funding

EPSRC Centre for Doctoral Training in Health Data Science EP/S02428X/1EPSRC Doctoral Training Partnership EP/W524311/1EPSRC Turing AI Fellowship: Ultra Sound Multi-Modal Video-based Human-Machine Collaboration EP/X040186/1
6 · The paper itself

Abstract

Artificial intelligence is increasingly embedded in clinical pathways, making effective human-AI collaboration (HAIC) a practical and policy priority in healthcare. We conducted a scoping review of empirical studies of HAIC in healthcare published from January 2015 to October 2025, using Joanna Briggs Institute methodology and PRISMA-ScR reporting. Of 17,463 records identified, 140 studies were included. Evidence was concentrated in diagnostic interpretation, with fewer studies in screening and triage, therapeutic decision-making, and administrative workflows. Effectiveness was defined inconsistently across task contexts and was usually assessed using short-term task-level metrics rather than patient or system outcomes. Most studies, particularly in diagnostic interpretation, reported benefits for human-AI teams. These depended on task fit, workflow integration, training, and appropriately calibrated trust. Ethical and governance issues, including accountability and patient safety, were often discussed but rarely evaluated empirically. These findings provide a foundation for more task-specific, longitudinal, and governance-aware evaluation of HAIC in healthcare.

Identifiers

PMID42323426
PMCPMC13642898

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.