P.070 Using AI to revolutionize clinical training through OSCE-GPT: a focused exploration of user feedback on otolaryngology and neurology cases
Bibliographic record
Abstract
Background: OSCE-GPT (https://learnmedicine.ca/) is an AI-based app that integrates history, physical exam, and relevant components for case guidance across medical disciplines to help trainees improve clinical skills. With global users across 60+ countries, this preliminary quality improvement study gathers user feedback on neurology and otolaryngology cases. Methods: A survey was distributed to users at the University of Ottawa and Cumming School of Medicine. Participants provided insights on the app’s use, perceived benefits, and suggested improvements. Results: Using 5-point Likert scales, 13 respondents, 9 of which evaluated an otolaryngology case, rated the overall usefulness of the learning tool 4.57± 0.51 (1=very poor, 5=very good), with a score of 4.00±0.65 relative to other teaching methods, such as didactic lectures or grand rounds (1=much worse, 5=much better). Users noted realistic interactions and self-paced learning as beneficial factors. Areas for improvement included a more fluid transition between physical exams and history, geographic variations in cases, and the addition of elements such as non-verbal patient cues or emotional. Conclusions: This study demonstrates utility of OSCE-GPT for medical trainees, particularly for otolaryngology and neurology cases. As cases continue to be added, feedback will be implemented to further improve user experience.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".