AI-PROGNOSIS aims to develop an AI-enabled digital health ecosystem to advance Parkinson's disease (PD) diagnosis and care. The ecosystem will comprise three tools:

1.mAI-Health

• Purpose: Mobile app individuals without PD to track their personalised risk of acquiring the disease.

• Functionality: Provides quantitative PD risk assessments based on user profiles, smartphone/watch-tracked digital biomarkers (dBMs), and occasional self-reports, in collaboration with attending physicians.

2. mAI-Care 

• Purpose: Mobile app for people with PD to track disease progression and medication efficacy.

• Functionality: Enables PwP and their caregivers to track symptoms and treatment effects and access personalised projections of PD progression using smartphone/watch-tracked dBMs data, self-reports, and clinical data.

3. mAI-Insights

• Purpose: Web app for healthcare professionals (HCPs) to support PD screening, patient follow-up, and medication optimisation.

• Features: 

  
  

  • Enables non-expert (e.g., general practitioners) and expert HCPs (neurologists, movement disorder specialists) to track and identify persons at risk of PD, with explainable estimations of the PD risk assessment model based on clinical data and data shared by users via mAI-Health (dBMs data and self-reports);

  • Provides expert HCPs with tools to monitor patients' status, view PD progression projections, and receive individualised predictions of patients' response to medication regimens through the AI-assisted Medication Decision support (AIMED) module.

The AI-PROGNOSIS toolkit will be shaped based on stakeholder feedback received during the user research and co-creation sessions and research outcomes from work package WP3 “Predicting PD risk, progression, and medication response”, integrating advancements in digital biomarkers and predictive models.