Artificial Intelligence and Medical Education (2013-2024): A Scopus-Based Bibliometric Analysis
Bibliographic record
Abstract
Background: Artificial Intelligence (AI) is transforming medical education by enabling personalised learning, adaptive feedback, simulation-based training, and automated assessments. While AI offers significant benefits, including curriculum optimisation and virtual tutoring, concerns around data privacy, access, and ethical implementation persist. Although bibliometric studies have explored AI in healthcare, comprehensive analyses of global collaboration and publication trends in AI-focused medical education remain limited. Aim: This study aims to analyse global research trends, key contributors, and thematic developments in the application of AI within medical education. Methods: A bibliometric analysis was conducted using the Scopus database. The search strategy included terms such as "Medical Education", "Artificial Intelligence", "Machine Learning", "Deep Learning", "Clinical Training", "Virtual Patients", and "Simulation". Data were analysed using the Bibliometrix R package to assess publication volume, keyword co-occurrence, author collaboration, and citation patterns. Results: Research output on AI in medical education has grown significantly, peaking in 2024 with 1,081 publications. The United States leads in publication volume, followed by Russia and Canada. "Artificial Intelligence" was the most frequently used keyword. Co-authorship and co-citation networks revealed strong international collaboration, with emerging themes in clinical competence, virtual simulation, and ethical considerations. Conclusion: The future of artificial intelligence in medical education is promising, with applications in personalised treatment plans, drug development, and virtual healthcare assistants. AI has transformative potential for medical education, particularly in personalised learning and simulation-based training. Strategic investment in AI literacy, ethical frameworks, and infrastructure is essential to ensure equitable and effective integration across global contexts.
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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 | 0.013 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.258 | 0.338 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".