Bibliographic record
Abstract
Introduction:After completing medical school, to become a board-certified surgeon in the United States, trainees must complete a 5 to 7 year period of training known as residency.This time focuses on subspecialty rotations, operative responsibility, and competency evaluation.Traditionally dependent on direct supervision and hands-on experience, with the rise of artificial intelligence (AI), training is changing, leading to discussions about its role in medical education.The DECODE framework provides guidance for integrating digital skills and ensuring AI supports technical abilities while also promoting professionalism, ethics, and patient centered care.This paper explores how AI impacts surgical residency training. Methods:A systematic review was conducted using PubMed (MEDLINE) with the search terms "surgery residency training AND artificial intelligence."Studies published in English between January 2020 and January 2025 were screened according to modified PRISMA guidelines.Twelve studies met the inclusion criteria and covered various surgical specialties and international programs.Results: Four main themes emerged: accuracy, efficiency, skill development, and training efficiency.AI-assisted platforms improved precision in simulations, standardized assessments, reduced faculty workload, and created adaptive learning paths.These features shortened learning curves, improved cognitive and technical skills, and boosted residents' confidence.Conclusion: AI has the capacity to change surgical education by standardizing training, expanding access, and enhancing outcomes.By aligning with DECODE, AI's role in residency programs shows how technology can strengthen the foundations of medical education, which has implications for other areas of healthcare.
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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 | 0.027 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".