Bridging the Gap Between AI Research and Real-World Healthcare
On September 6, 2025, Shreyasi Pathak successfully defended her thesis at the University of Twente, marking a significant step forward in the practical application of AI in everyday healthcare. Conducted in collaboration with Ziekenhuisgroep Twente (ZGT), her research introduces the concept of “reality-centric AI”-models designed from the outset to meet the needs of real hospital environments.
Key Insights
Real-World Data: AI models must be trained on datasets that reflect actual hospital conditions. Pathak’s work includes a privacy-preserving “model-to-data” platform that allows global researchers to train models on sensitive hospital data without having direct access to it. This platform uses one of the largest mammography datasets from ZGT.
Adapting to Clinical Workflows: Many AI models fail in practice due to their reliance on manually annotated data or limited patient groups. Pathak’s research demonstrates how models can leverage “weak labels” (existing hospital data), handle diverse patient cases, and integrate multiple data sources to enable more robust diagnostics.
Explainability and Trust: For clinicians to trust AI, models must clearly explain their decisions. This thesis evaluates interpretability techniques to ensure that AI aligns with medical expertise and provides transparent, actionable outputs.
Clinical Applications
The research was applied across three clinical use cases: breast cancer diagnosis using mammography images, sleep stage prediction from electrophysiological signals, and 30-day post-operative mortality prediction in elderly hip fracture patients.
Looking Ahead
Pathak envisions AI systems that can diagnose, recommend the most appropriate tests, explain their reasoning, and flag uncertain cases for clinician review, enabling true collaboration between humans and machines. This collaboration between the University of Twente and ZGT paves the way for powerful, practical AI ready to tackle real-world healthcare challenges.