Researchers introduced a video‑based system that quantifies finger‑tapping performance in Parkinson’s disease using interpretable computer vision and machine‑learning readouts. The tool delivers granular, objective metrics aligned with clinical motor assessments and could be used for remote monitoring, trial endpoints, and longitudinal disease tracking. By removing the need for specialized hardware, the approach may lower barriers to scalable digital biomarker deployment in neurology trials and routine care.