Evidence map›Paper›PMID 39720600›Full record

ReviewEClinicalMedicine2025

AI education for clinicians.

Tim Schubert, Tim Oosterlinck, Robert D Stevens, Patrick H Maxwell, Mihaela van der Schaar

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing 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

43 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. What do clinicians edit in ambient AI-drafted clinical documentation? A qualitative content analysis.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  7. Review
  8. Review
  9. Review
  10. Review
  11. Article
  12. Large language models in healthcare.Nature reviews. Nephrology · 2026
    Article
  13. Review
  14. Article
  15. Review
  16. Review
  17. Article
  18. Article
  19. AI-powered tools in family medicine: Bridging technology and practice.Journal of family medicine and primary care · 2026
    Article
  20. Review
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

5 authors.

Tim SchubertDepartment of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
Tim OosterlinckDepartment of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
Robert D StevensDepartments of Anesthesiology and Critical Care Medicine, Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.
Patrick H MaxwellSchool of Clinical Medicine, University of Cambridge, Cambridge, UK.
Mihaela van der SchaarDepartment of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid advancements in medical AI necessitate targeted educational initiatives for clinicians to ensure AI tools are safe and used effectively to improve patient outcomes. To support decision-making among stakeholders in medical education, we propose three tiers of medical AI expertise and outline the challenges for medical education at different educational stages. Additionally, we offer recommendations and examples, encouraging stakeholders to adapt and shape curricula for their specific healthcare setting using this framework.

Indexed as

Artificial intelligenceCliniciansFrameworkMachine learningMedical education

Identifiers

PMID39720600
PMCPMC11667627

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.