828 Real-Time Artificial Intelligence Instruction in Comparison to Human Expert Instruction in Surgical Technical Skills Teaching – a Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Aim Surgical simulators equipped with artificial intelligence (AI) systems provide risk-free training on realistically simulated complex patient cases, objective performance assessment, and tailored error avoidance feedback. This study compared the efficacy of real-time AI instruction with human instructor-mediated training in surgical technical skills teaching. Method Medical students (n = 98) were randomly allocated into three feedback groups. Everyone repeated a virtual brain tumour resection five times. The first repetition was done without receiving feedback to assess their baseline performance. Students in Group 1 did not receive real-time feedback, and they were shown visual feedback only after each procedure based on expert benchmarks. Students in Group 2 were instructed in real-time by the AI system. After each task, the students were shown their error-video clips generated by this system alongside the expert-level demonstrations relating to each error. Students in Group 3 were instructed by human instructors during the tasks. After each task, instructors summarized the areas of improvement and demonstrated correction techniques. All participant data was scored using a composite-score. Statistical analysis was conducted to compare learning from the first to the last task repetition. Results Students in Group 2 and Group 3 significantly improved in the performance score by the third and second repetition, respectively (p<0.01, p = 0.01) compared to their baseline performance. Students in Group 2 achieved significantly higher scores than those in Group 3 in the fifth repetition (p<0.01). Conclusions Artificial intelligence systems may provide competency-based curricula in surgical training with efficient surgical technical skills teaching methodologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".