Cited by 12Artificial Intelligence in Healthcare and EducationSimulation-Based Education in HealthcareClinical Reasoning and Diagnostic Skills

Generative AI for clinical reasoning: A scoping review

Liu, Ying-Mei, Chou, Chang-Chuan, Jaing, Tang-Her, Okoli, Chizimuzo T.C.·2026·Teaching and Learning in Nursing

Integrity signals

Registry

Crossref · 10.1016/j.teln.2025.08.008

Cites

42 of 42 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Liu, Y.-M., Chou, C.-C., Jaing, T.-H., & Okoli, C. T. C.
Affiliations
  • Chang Gung University of Science and Technology (TW)
  • Linkou Chang Gung Memorial Hospital (TW)
  • University of Kentucky (US)
Venue
Teaching and Learning in Nursing
Publisher
Elsevier BV
Published
2026-01
Volume
21
Issue
1
Pages
e305-e312
ISSN
  • 1557-2013 (ISSN-L)
  • 1557-3087
OpenAlex
W4414090115
Cited by
12
References
42
Citations by year
2026: 10 · 2025: 2
FWCI
2.89
Type
journal article
Language
en
Open access
closed
Locations
Teaching and learning in nursing (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Physiology › Simulation-Based Education in Healthcare
  • Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
Keywords
generative AI, clinical reasoning, simulation-based education, undergraduate health professions education, virtual patients, AI-generated feedback, expert feedback, inquiry-based learning, AI in teaching

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review