Ambient Audio-Visual Capture for Clinical Documentation and Assessment

Starting soon · Not applicable

Conditions studied: Clinical Documentation, Artificial Intelligence, Surgical Education, Medical Education

In brief

AI-powered tools that automatically document clinical conversations are being adopted rapidly in outpatient settings but have not been evaluated in hospital wards. Existing tools use audio recording only, which cannot capture physical examination findings, procedural observations, or clinical safety behaviours - elements of a ward round that are visible but not audible. This study evaluates an ambient audio-visual (AV) capture system - BlackFrame - that uses both microphone and camera to generate accurate clinical documentation and structured educational feedback in a real inpatient surgical ward setting. Medical students and doctors in training participate in supervised ward round encounters with consenting adult inpatients. The BlackFrame AI platform generates: (a) a structured draft clinical note for the supervising clinician to review and countersign before any use in the patient record; and (b) formative feedback for the trainee, delivered within 30 minutes, covering clinical communication, examination technique, and documentation quality. The study measures whether AI-generated feedback improves trainee clinical performance over a placement, how much documentation time is saved, and whether the system is acceptable to patients and clinicians. No AI-generated text enters the patient record without explicit clinician review and sign-off. All participation is voluntary.

Key facts

Study ID

NCT07649772

Run by

BlackFrame.ai

People needed

60

Starts

2026-09-01

Expected to finish

2026-11-30

Last updated by the study team

2026-06-16

Who can join

Age: any. Sex: any. Healthy volunteers: accepted.

Full record on ClinicalTrials.gov

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