COMFORT-Computational Models FOR patienT stratification in urologic cancers – Creating robust and trustworthy multimodal AI for health care
Dnr:
NAISS 2025/22-1267
Type:
NAISS Small Compute
Principal Investigator:
Zhou Zhou
Affiliation:
Umeå universitet
Start Date:
2025-10-01
End Date:
2026-10-01
Primary Classification:
10210: Artificial Intelligence
Webpage:
Allocation
Abstract
The project strives to develop transparent and accurate computational models by integrating complex health data from multiple sources. These models will use advanced AI-powered risk stratification methods to help healthcare professionals select the right treatments, prevent disease progression, and improve the patient journey. Ultimately, the project will produce the first multi-national evaluation of AI models in a clinical setting and offer new insights to maximise the usefulness and acceptance of the technology.