ArticleNPJ digital medicine2026
Vision-Enabled AI scribes reduce omissions in clinical conversations: evidence from simulated medication histories.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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17 authors.
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Abstract
Most ambient AI medical scribes process audio only, omitting clinically important visual details. We developed a vision-enabled AI scribe using Google's Gemini model and Ray-Ban Meta smart glasses to document medication histories-a task requiring both audio and visual input. Ten clinical pharmacists video-recorded 110 simulated medication history interviews. Following iterative prompt engineering on 10 training recordings, the scribe was evaluated on 100 test recordings (2160 data points) across patient details and medication-specific fields. The vision-enabled scribe achieved 98% overall accuracy (2114/2,160 data points), ranging from 96% for patient details to 99% for dosing directions and indication. Video input significantly outperformed audio-only processing (98% vs 81%, P < 0.001), primarily through reduced omissions (10 vs 358 errors). Vision-enabled AI scribes substantially improved documentation accuracy for tasks requiring visual input, demonstrating potential to markedly reduce omission errors in clinical documentation.
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