Quantifying device type and handedness biases in a remote Parkinson's disease AI-powered assessment.

We investigate issues pertaining to algorithmic fairness and digital health equity within the context of using machine learning to predict Parkinson's Disease (PD) with data recorded from structured assessments of finger and hand movements. We evaluate the impact of demographic bias and bias related to device type and handedness. We collected data from 251 participants (99 with PD or suspected PD, 152 without PD or any suspicion of PD).

Parkinson Disease Recognition Using a Gamified Website: Machine Learning Development and Usability Study.

Background: Parkinson disease (PD) affects millions globally, causing motor function impairments. Early detection is vital, and diverse data sources aid diagnosis. We focus on lower arm movements during keyboard and trackpad or touchscreen interactions, which serve as reliable indicators of PD.