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Automatic assessment of impairment and disease severity is a key challenge indata-driven medicine. We propose a novel framework to address this challenge,which leverages AI models trained exclusively on healthy individuals. TheCOnfidence-Based chaRacterization of Anomalies (COBRA) score exploits thedecrease in confidence of these models when presented with impaired or diseasedpatients to quantify their deviation from the healthy population. We appliedthe COBRA score to address a key limitation of current clinical evaluation ofupper-body impairment in stroke patients. The gold-standard Fugl-MeyerAssessment (FMA) requires in-person administration by a trained assessor for30-45 minutes, which restricts monitoring frequency and precludes physiciansfrom adapting rehabilitation protocols to the progress of each patient. TheCOBRA score, computed automatically in under one minute, is shown to bestrongly correlated with the FMA on an independent test cohort for twodifferent data modalities: wearable sensors ($\rho = 0.845$, 95% CI[0.743,0.908]) and video ($\rho = 0.746$, 95% C.I [0.594, 0.847]). Todemonstrate the generalizability of the approach to other conditions, the COBRAscore was also applied to quantify severity of knee osteoarthritis frommagnetic-resonance imaging scans, again achieving significant correlation withan independent clinical assessment ($\rho = 0.644$, 95% C.I [0.585,0.696]).

Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects