Combining real-time AI and in-person expert instruction in simulated surgical skills training - Randomized crossover trial
Bibliographic record
Abstract
Abstract Traditional surgical training has significant limitations, lacking objectivity and standardization. Deploying AI tools with conventional expert-mediated teaching may uncover areas where AI could complement experts and enhance surgical training through real-time performance assessment and feedback alongside risk mitigation. This randomized crossover trial assessed learning outcomes in two training sessions involving in-person expert instruction and real-time AI feedback using previously validated tumor resection simulations. Receiving expert feedback before real-time AI instruction led to greater performance improvement in trainee performance scores compared to the opposite order, with a mean difference of 0.67 95%CI [0.43–0.91], p < 0.001. Diminishing returns were observed with human expert feedback, which were not seen with AI feedback, such as increased injury and bleeding risk. In surgical procedural training, AI feedback may efficiently maintain peak performance after an initial learning phase led by human experts. AI-integrated surgical curricula should consider the relative benefits of both AI and expert feedback.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".