What question did this study set out to answer?

To develop and evaluate an automated artificial intelligence pipeline capable of assessing resident clinical skills directly from raw consultation audio with real-time feedback.

August 16, 2026npj Digital MedicineOpen Access

CliniSense AI for automated clinical skills assessment with real-time feedback

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Authors

AKAnam Nawaz KhanUniversity of Tennessee at Knoxville JTJose TupayachiOak Ridge National Laboratory WDWilliam DabbsKnoxville College

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Implication

Validation study shows automated audio evaluation achieves high accuracy in simulated clinical encounters, suggesting scalable real-time feedback for medical trainees.

Key Points

To develop and evaluate an automated artificial intelligence pipeline capable of assessing resident clinical skills directly from raw consultation audio with real-time feedback.
Engineered an edge-deployable pipeline integrating automatic speech recognition, speaker diarization, role detection, and multi-label competency classification.
Evaluated performance on N=270 publicly available simulated clinical consultations compared against majority-vote ground truth.
The classification ensemble achieved a Cohen’s κ of 0.824 against majority-vote ground truth, surpassing mean human annotator agreement (κ = 0.721) with sub-1.5-s streaming latency.
Identified unexpressed diagnostic reasoning in 10.3% of encounters and captured a quantifiable trade-off between information gathering and patient-centered communication.

Cite This Study

Khan et al. (2026) studied this question.

synapsesocial.com/papers/6a817aa1f2fb91fc834ae9cf https://doi.org/10.1038/s41746-026-03100-8

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Implication

Validation study shows automated audio evaluation achieves high accuracy in simulated clinical encounters, suggesting scalable real-time feedback for medical trainees.

CliniSense AI for automated clinical skills assessment with real-time feedback

Discussion

Member takes

Implication

Key Points

Cite This Study

CliniSense AI for automated clinical skills assessment with real-time feedback

Discussion

Member takes

Implication

Key Points

Cite This Study

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