Imagine a smoke detector, but for mental health. By listening to short voice recordings, an AI can spot tiny changes in how someone speaks that signal a relapse is coming. This lets doctors step in early to help before a crisis happens.
Clinicians cannot monitor schizophrenia patients frequently enough to catch relapses early. This leads to emergency hospitalizations and a reliance on long-term medication that patients often want to avoid.
A beta AI monitoring application that analyzes short speech samples to detect signs of psychosis. It includes a value framework for reimbursement and a user-assessment survey.
If you are an mHealth app developer dealing with high patient churn in chronic care — this project developed an AI monitoring tool that detects subtle speech deviations to predict relapse. This allows for timely intervention and improved clinical outcomes.
If you are a private psychiatric clinic dealing with the inefficiency of frequent manual check-ups — this project developed a home-based monitoring system that alerts clinicians of deterioration. This enables a safer discontinuation of long-term medication for patients.
If you are a health insurance provider dealing with the high direct and indirect costs of schizophrenia — this project developed a value framework and predictive tool to reduce expensive emergency relapses. This optimizes public and private financing systems.
Based on available project data, the project is investigating direct and indirect costs of schizophrenia and reimbursement criteria in public financing systems to develop a value framework, but no specific price point is listed.
Yes, the tool is being validated across six languages and will be tested in a large, multilingual population starting in July 2025.
Based on available project data, the IP details are not specified, though the project involves a consortium of 13 partners including 2 industry entities.
A beta version has already been created and tested; a large-scale prospective trial is scheduled to begin in July 2025, with the project running until December 2028.
The system is designed as a user-friendly application used from home that delivers a message to clinicians when predictive speech deviations are detected.
The consortium is heavily research-driven with 8 universities and 1 research institute, but maintains a 15% industry ratio with 2 industrial partners and 1 SME. This balance suggests a strong scientific foundation with a clear path toward commercialization, supported by partners across 11 countries.
Contact Academisch Ziekenhuis Groningen in the Netherlands
Contact us to explore licensing opportunities for the AI speech-monitoring beta.
