How AI Is Transforming Medical Education: Bibliometric Analysis
Bibliographic record
Abstract
BACKGROUND: Artificial intelligence (AI) is increasingly being integrated into medical education. As AI technologies continue to evolve, they are expected to enable more sophisticated student tutoring, performance evaluation, and reforms of curricula. However, medical education entities have been ill-prepared to embrace this technological revolution, and there is anxiety concerning its potential harm to the community. OBJECTIVE: To explore research trends in the field and identify future directions for AI-enabled medical education, we conducted a systematic bibliometric analysis focusing on temporal trajectories in the field. METHODS: Documents were collected from the Web of Science and Scopus databases covering the period from 2000 to 2024. A multistep search strategy combining information retrieval, a definitive journal list, and cocitation analysis was used to identify relevant publications. Journal and author impact were assessed using both publication and citation metrics. Research trends and hot spots were examined through citation burst detection, frequency analysis, and co-occurrence networks, with a color gradient used to indicate the average occurrence year of keywords. The citation lineage structure of the field was evaluated using a k-means clustering-based analysis of cocitation networks to trace influential references. RESULTS: Our analysis revealed a significant increase in publications since 2021, with foundational works emerging as early as 2019. Influential journals in this domain included JMIR Medical Education, Anatomical Sciences Education, and Medical Education. The evolving research trajectory exhibited a shift from conventional computer-assisted learning tools toward generative AI platforms. Earlier applications of AI in medical education were predominantly concentrated at the undergraduate level, indicating substantial potential for expansion into graduate and continuing medical education. Furthermore, limited cocitation connections were observed between recent generative AI research and conventional medical AI studies, and investigations into medical students' attitudes toward generative AI remain scarce. CONCLUSIONS: There are critical needs for (1) interdisciplinary studies that intentionally integrate generative AI with foundational medical AI work and (2) involving medical educators and students in AI development. Future research should focus on building theoretical frameworks and collaborative projects that connect these currently separate domains to foster a more cohesive knowledge base.
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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 | 0.016 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.174 | 0.241 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".