Artificial intelligence in health professions education: <i>A state-of-the-art meta-review</i>
Bibliographic record
Abstract
Abstract The growing adoption of artificial intelligence (AI) technologies in healthcare is transforming modern healthcare systems, necessitating current and future healthcare providers to be educated on the meaningful use of AI in their academic and professional activities. Despite an emerging body of literature emphasizing the use of AI in health professions education (HPE) and the availability of multiple reviews on this topic, there is a lack of meta-research evidence that can provide a broader overview of the evidence landscape reported across the existing systematically conducted literature reviews. This meta-review aimed to synthesize evidence on the applications of different AI technologies in HPE, multi-level factors influencing the applications of AI in HPE, and associated outcomes from existing systematically conducted literature reviews (SCLRs). A total of 48 eligible SCLRs were identified from six databases and additional sources, and the synthesized findings suggest emerging use cases of multiple AI technologies among HPE users and institutions, including AI-assisted instructional delivery, augmenting learning sessions, content optimization, and providing feedback. While most reviews reported positive HPE-related outcomes, there are critical challenges at the user and institutional levels, which should be considered for effective AI implementation in HPE. Building AI capacities among HPE users and facilitating AI resources development are critical for AI adoption. This meta-review may inform HPE and broader healthcare communities to advance knowledge and practice on evidence-based AI in HPE settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".