Bridging artificial intelligence and ethics in medical education: a comprehensive perspective from students and faculty
Bibliographic record
Abstract
IntroductionArtificial Intelligence (AI) simulates human intelligence, solves problems, and performs speedy calculations and evaluations based on previously assessed data [1].It can perform automated tasks without requiring every step in the process to be explicitly programmed by a human [2].Machine Learning (ML), a subset of artificial intelligence, uses algorithms that distinguish patterns in data without explicit programming.Deep Learning (DL) is a branch of machine learning that uses artificial neural networks as algorithms to learn from data and make predictions or judgments.It is especially helpful for tasks like audio and image recognition [3].The groundbreaking technology of AI has increasingly infiltrated numerous fields, including industries such as agriculture, manufacturing, fashion, and sports, with healthcare and the medical system being significant among them [4].Artificial intelligence has emerged as a powerful tool in the medical field, revolutionizing the health industry and enhancing patient care.The nexus between artificial intelligence and medicine has aroused
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".