Artificial Intelligence in UK Medical Education: A Framework for Curriculum Reform (Preprint)
Bibliographic record
Abstract
BACKGROUND Artificial intelligence (AI) is increasingly transforming healthcare through improvements in diagnosis, predictive analytics, and workflow optimisation. However, there remains a significant gap in AI training within UK medical education, leaving future clinicians underprepared for AI-driven healthcare environments. OBJECTIVE This review investigates global best practices for AI integration into medical education and proposes a structured framework for embedding AI into the UK medical curriculum. It aims to assess current attitudes, highlight existing knowledge gaps, and recommend practical implementation strategies. METHODS An analysis of international case studies (e.g., Stanford, University of Toronto, CUHK) was conducted alongside a review of teaching methodologies, stakeholder perspectives, and UK-based surveys to identify core competencies and challenges in AI education. RESULTS Effective integration strategies include the use of AI-powered simulations, interdisciplinary collaboration, elective modules, and faculty training. Major barriers include lack of AI-literate educators, insufficient ethical training, and limited infrastructure. Knowledge gaps persist among students and faculty in areas such as algorithmic bias, AI ethics, and clinical decision-making. CONCLUSIONS To meet the demands of modern healthcare, the UK medical curriculum must adopt comprehensive AI training. This includes practical exposure, ethical awareness, and stakeholder engagement. Proactive reform will ensure graduates are equipped to critically and ethically apply AI tools in clinical practice.
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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 | 0.042 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".