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Towards accurate and interpretable competency-based assessment: enhancing clinical competency assessment through
Sapir Gershov1,2, Fadi Mahameed3,4, Aeyal Raz3,5
1Technion Autonomous Systems Program, Technion - Israel Institute of Technology, Haifa, Israel.
NPJ Digital Medicine
|February 3, 2026
Summary
Artificial Intelligence (AI) offers objective competency assessments in medical education. A new AI framework using video, audio, and monitor data accurately evaluates anesthesia residents, improving training fairness and reliability.
Area of Science:
- Medical Education Technology
- Artificial Intelligence in Healthcare
- Simulation-Based Training
Background:
- Current medical competency assessments are subjective and resource-intensive.
- There is a need for objective, reliable, and transparent evaluation methods in medical training.
- Artificial Intelligence (AI) presents a potential solution to these challenges.
Purpose of the Study:
- To introduce a multimodal AI framework for objective competency assessment in medical education.
- To quantify deviations from ideal performance benchmarks using an anomaly detection model.
- To provide interpretable feedback for simulation-based training.
Main Methods:
- Developed a multimodal AI framework integrating video, audio, and patient monitor data.
- Trained an anomaly detection model (MEMTO) on data from 90 anesthesia residents.
- Utilized SHAP analysis to identify key performance drivers.
Main Results:
- AI-derived competency scores strongly correlated with expert ratings (Spearman's ρ = 0.78, ICC = 0.75).
- The model demonstrated high ranking precision (Relative L2-distance = 0.12).
- SHAP analysis highlighted communication and eye contact as significant factors in performance variability.
Conclusions:
- The AI framework provides objective and interpretable competency assessments.
- This approach enhances fairness, reliability, and transparency in simulation-based medical education.
- The study offers evidence for integrating AI into scalable and equitable medical training evaluations.
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