CoChat
Sign Up
Cited by 17Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationArtificial Intelligence in Healthcare

Medical student knowledge and critical appraisal of machine learning: a multicentre international cross‐sectional study

Blacketer, Charlotte, Parnis, Roger, B. Franke, Kyle, Wagner, Morganne, Wang, David, Tan, Yiran, Oakden‐Rayner, Luke, Gallagher, Steve +6 more·2021·Internal Medicine Journal
DOI 10.1111/imj.15479
Open this paper in CoChat — free account

Abstract

To utilise effectively tools that employ machine learning (ML) in clinical practice medical students and doctors will require a degree of understanding of ML models. To evaluate current levels of understanding, a formative examination and survey was conducted across three centres in Australia, New Zealand and the United States. Of the 245 individuals who participated in the study (response rate = 45.4%), the majority had difficulty with identifying weaknesses in model performance analysis. Further studies examining educational interventions addressing such ML topics are warranted.

Integrity signals

Registry

Crossref · 10.1111/imj.15479

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

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

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Quality improvement in AI education is reflected through critical appraisal of AI literature and tools, physician involvement in AI tool design/development, ability to identify and control for bias, communication with data scientists and engineers, and communication with patients.

    “Quality improvement was reflected through the need for critical appraisal of AI literature and tools [12,13,34,37,39], physician involvement in AI tool design/development [13,39], the ability to identify and control for bias [45], communication with data scientists and engineers [13], and communication with patients [12,13,37,41,42].”

    From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [34]

  • A strong theoretical base of AI for practice would equip learners with skills needed to critically appraise AI literature and tools before implementing them, interpret model output, and communicate AI findings to others.

    “This would equip them with the skills needed to critically appraise AI literature and tools before implementing them [12,13,34,37,39], interpret model output [13,37,42], and communicate AI findings to others [12,13,42].”

    From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [34]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Blacketer, C., Parnis, R., B. Franke, K., Wagner, M., Wang, D., Tan, Y., Oakden‐Rayner, L., Gallagher, S., Perry, S. W., Licinio, J., Symonds, I., Thomas, J., Duggan, P., & Bacchi, S.
Affiliations
  • Royal Adelaide Hospital (AU)
  • Royal Darwin Hospital (AU)
  • University of Adelaide Adelaide South Australia Australia
  • SUNY Upstate Medical University (US)
  • University of Otago (NZ)
  • State University of New York (SUNY) Upstate Medical University Syracuse New York USA
  • The University of Adelaide (AU)
Corresponding author
Bacchi, Stephen
ORCID
Venue
Internal Medicine Journal
Publisher
Wiley
Published
2021-09
Volume
51
Issue
9
Pages
1539-1542
ISSN
  • 1444-0903 (ISSN-L)
  • 1445-5994
DOI
10.1111/imj.15479
OpenAlex
W3201451556
Cited by
17
References
5
Citations by year
2026: 1 · 2025: 4 · 2024: 5 · 2023: 5 · 2022: 2
FWCI
0.87
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 18 d)
Locations
  • Internal Medicine Journal (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
machine learning, medical students, critical appraisal, clinical decision support, model evaluation, medical education, formative assessment
MeSH terms
Machine Learning, Australia, Cross-Sectional Studies, Curriculum, Education, Medical, Undergraduate, Humans, Students, Medical, United States

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