Vision wearables with artificial intelligence to close the sensory gap in patient characterization

Artificial intelligence (AI) for precision medicine remains constrained by sparse, subjective data. Clinician-worn, egocentric audio-visual sensing with multimodal AI is poised to capture movement, affect, and context. We propose a translational framework from continuous capture to feature extraction, digital biomarkers, and eventual real-time alerts that supports the generation of objective clinical outcome types. We further outline clinical usability, validation, and ethical challenges around the deployment of vision wearables.