Imagine having a smart health assistant that catches Parkinson's before the obvious shakes start by spotting tiny patterns in your daily movement. It works like a weather forecast for your health, predicting how the disease will progress and which medicine will actually work for you. Instead of guessing with trial-and-error drugs, doctors get a clear digital map of the patient's needs.
Parkinson's is often misdiagnosed due to subtle early signs, and finding the right medication is a slow trial-and-error process. This leads to patient suffering and high costs due to medication non-adherence.
An ecosystem of three tools: mAI-Health (screening), mAI-Care (patient management), and mAI-Insights (decision-support for doctors).
If you are a health app developer dealing with low user retention in chronic care — this project developed the mAI-Health and mAI-Care apps that provide tailored insights for informed health management. This allows users to track symptoms in daily living via digital biomarkers.
If you are a wearable tech manufacturer dealing with a lack of clinical utility for your sensors — this project developed a system of biomarkers that track key risk and progression markers. This transforms a simple wristband into a medical-grade tool for PD screening.
If you are a drug development firm dealing with costly trial-and-error medication regimens — this project developed predictive AI models for medication response. This helps identify the right patient groups for specific treatments based on genetic and phenotypic data.
Based on available project data, the specific pricing or cost of the toolkit is not mentioned; it is currently an EU-funded research project.
The project uses a consortium of 19 partners across 11 countries and leverages large databases like PPMI and CPP, suggesting a design intended for broad scalability.
Based on available project data, specific licensing terms are not provided, though the project follows a Trustworthy AI development framework.
Yes, the project specifically aims to create a privacy-aware AI-driven toolkit and uses the OMOP common data model for data harmonisation.
The project period runs from 2023-07-01 to 2027-06-30, indicating that final validated tools will be ready by mid-2027.
The consortium is well-balanced for commercialization, featuring a 37% industry ratio with 7 industrial partners, including 5 SMEs. With 19 partners across 11 countries, the project has strong cross-border validation and a mix of 10 universities and 2 research centers to ensure scientific rigor before market entry.
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