Computational Models for Patient Stratification in Urologic Cancers – Creating Robust and Trustworthy Multimodal AI for Health Care

Abstract

Current clinical approaches fail to fully utilize unstructured data in managing prostate cancer (PCa) and kidney cancer (KC), leading to inefficiencies in patient care and increased costs. Effective diagnostics and treatments depend on integrating multimodal data, yet progress is hampered by limited data accessibility and a lack of collaborative validation between clinicians and computer scientists. To address these challenges, the EU-funded COMFORT project aims to develop commercially viable, data-driven multimodal decision support systems. These systems will improve clinical prognostication, patient stratification, and personalized treatment while also assessing the trust that healthcare professionals and patients place in AI-driven tools.

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