PARK: Remote AI Screening for Parkinson’s Disease

We developed PARK, a multimodal AI-driven remote screening tool that identifies Parkinson’s disease from webcam-based recordings of speech, facial expression, and motor tasks. PARK was evaluated across three independent datasets spanning supervised and unsupervised settings, demonstrating strong classification performance and high usability in diverse populations.

• Leveraged short standardized tasks (speech, facial expression, finger tapping) captured via webcam and extracted clinically relevant features for multimodal analysis.
• Evaluated PARK on 1,865 participants (including 670 with PD) across multiple real-world cohorts, with AUROC between 0.85–0.87 and accuracy ~80–83% on held-out test sets.
• Demonstrated balanced performance across demographic subgroups (age, sex, ethnicity) and agreement with neurologist assessments on external validation subsets.
• Designed uncertainty-aware prediction mechanisms that withhold low-confidence outputs to support safe use in unsupervised, at-home deployment.
• Collected structured usability feedback showing high participant satisfaction and perceived utility in both supervised and home settings.