Ambient AI for Precision Medicine
In collaboration with Kevin Johnson, MD’s AI for Ambulatory Care Innovation lab, we’re reimagining what patient care would look like if the clinic were instrumented with modern robotic sensors, enabling real-time clinical decision support. Throughout the encounter, and even before the provider entered the room, multimodal AI models would quantify the patient’s physical, cognitive, and emotional health, augmenting the clinical exam, and providing for quantitative longitudinal assessment. As first steps toward this goal, we are developing ML models to characterize patient-provider interactions (Jang et al., 2025), patient gait, and cognitive issues, framing the problem as medical visual question answering (Park et al., 2025). Critically, we’re developing these methods to preserve patient privacy, ensure transparecy and explinability, and avoid interference with the clinician-patient relationship. We’re also exploring related approaches for spatio-temporal clinical understanding of surgery (Liao et al., 2025; Liao et al., 2025)(with Daniel Hashimoto, MD) and in trauma bays (with Jeremy Canon, MD).
