The AI-PROGNOSIS digital health ecosystem brings together three applications – mAI-Health, mAI-Care and mAI-Insights – which form an ecosystem of digital health tools for supporting Parkinson’s disease (PD) screening, disease management and clinical decision-making on medication selection and anticipated response.     

Building on earlier versions and refined technical specifications, the three applications have matured into functional Minimum Viable Products (MVPs) ready for real-world proof-of-concept validation. Their development was driven by updated user requirements and extensive co-creation activities with the patient panel and clinical partners, ensuring that patient-facing and clinician-facing features are aligned with their needs and workflows.    

The three applications address different aspects of Parkinson’s disease screening and care. mAI-Health is designed for people without PD to help them monitor their PD risk status under the supervision of a healthcare professional. The application collects data used by the AI-PROGNOSIS PD risk prediction model, with its output communicated and monitored by the attending healthcare professional through mAI-Insights.     

mAI-Care is designed for people diagnosed with PD who want to track their symptoms with support from their attending physician. Users have access to tracked digital biomarkers of certain symptoms, while selected data also serve as input to the AI-PROGNOSIS PD progression prediction model. The application includes digital measurements of key PD symptoms as well as educational information about life with PD and how technology can help with disease management.     

mAI-Insights is a web application for physicians, allowing them to monitor and follow up on people with PD and people at risk using data collected through mAI-Health and mAI-Care. It provides access to reports on PD risk and progression estimations, as well as the AI-assisted Medication Decision support (AIMED) module developed within AI-PROGNOSIS.     

Most planned functionalities across the ecosystem have now been implemented. These include smartwatch data capturing, self-reporting and self-logging assessments, active cognitive and motor function tests, digital biomarker graphs, and clinician-facing reports on PD risk, progression and medication response.

The AI-PROGNOSIS digital health ecosystem has progressed from early prototypes to a coherent, integrated MVP towards supporting PD risk monitoring and patient management. Remaining product features and data processing pipeline refinements are underway as work continues towards the final, refined MVPs.

Read the full report on Zenodo: https://zenodo.org/records/21103398