Improving medical education of risks of AI use in healthcare
Bibliographic record
Abstract
Artificial intelligence (AI) offers transformative potential in healthcare, enhancing drug discovery, data processing, early detection, and clinical decision-making. However, its adoption poses significant risks, including client harm, misuse, automation bias, perpetuation of inequities, and security issues. Effective integration requires ongoing collaboration among healthcare professionals, developers, policymakers, and ethicists to ensure accountability and transparency. As AI becomes more prevalent, medical education must evolve to equip students with a deep understanding of AI technology, risk assessment, and mitigation strategies. Currently, medical curricula fall short in this area, necessitating educational reforms. We propose diverse and comprehensive teaching methods, including case studies, interdisciplinary projects, hackathons, interactive workshops, and cross-cultural design thinking, to better prepare medical students. By fostering an interdisciplinary approach, these methods aim to build a foundation for responsible AI use in healthcare, ensuring future professionals are well- equipped to navigate its complexities and ethical challenges.
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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 | 0.037 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| 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".