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Peer-reviewedCC BYnpj Digital Medicine

Human-AI Collaboration in Healthcare: A Scoping Review

  • Joshua Strong1
  • Harry Rogers2
  • Emma Sun2
  • Anna Louise Todsen3
  • Jody Ede4
  • Cherry Lumley5
  • Nick Yeung3
  • Helen Higham6,7,5
  • J. Alison Noble2
7 affiliations
  1. 1Institute of Biomedical Engineering, University of Oxford, Oxford, UK. [email protected]
  2. 2Institute of Biomedical Engineering, University of Oxford, Oxford, UK
  3. 3Department of Experimental Psychology, University of Oxford, Oxford, UK
  4. 4Department of Health and Care Professions, University of Exeter, Exeter, UK
  5. 5Oxford University Hospital NHS Foundation Trust, Oxford, UK
  6. 6Nuffield Department of Anaesthesia, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
  7. 7Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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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.

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Cite

BibTeXRIS
BibTeX
@article{strong2026humanai,
  title = {{Human-AI Collaboration in Healthcare: A Scoping Review}},
  author = {Joshua Strong and Harry Rogers and Emma Sun and Anna Louise Todsen and Jody Ede and Cherry Lumley and Nick Yeung and Helen Higham and J. Alison Noble},
  journal = {npj Digital Medicine},
  year = {2026},
  month = jun,
  doi = {10.1038/s41746-026-02918-6},
  url = {https://doi.org/10.1038/s41746-026-02918-6},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Human-AI Collaboration in Healthcare: A Scoping Review
AU  - Joshua Strong
AU  - Harry Rogers
AU  - Emma Sun
AU  - Anna Louise Todsen
AU  - Jody Ede
AU  - Cherry Lumley
AU  - Nick Yeung
AU  - Helen Higham
AU  - J. Alison Noble
PY  - 2026
DA  - 2026/06/20
JO  - npj Digital Medicine
DO  - 10.1038/s41746-026-02918-6
UR  - https://doi.org/10.1038/s41746-026-02918-6
AB  - 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.
SN  - 2398-6352
ER  -