AI Centre Team Presents Advances in Federated Learning, Oncology Data Extraction, and Population Health at ISPOR Europe 2025

The AI Centre presented a series of technical developments at ISPOR Europe 2025, showcasing progress in federated learning for imaging and clinical data, large language model–based information extraction in Oncology, and new methodologies to support population health research across the NHS.

FLIP: Federated Learning Interoperability Platform

The team introduced updates to FLIP (Federated Learning Interoperability Platform), an open-source infrastructure designed to enable secure, hospital-retained analytics across NHS sites.

FLIP provides capabilities for federated cohort discovery, multi-modal data engineering — spanning structured data, natural language processing, and radiology — and AI model training without transferring sensitive patient data outside hospital boundaries. The platform supports deep phenotyping using un-curated, real-world datasets, addressing limitations of traditional, highly curated research environments.

Development of a Unified Medication Adherence Index

Researchers also reported findings on a proposed Medication Adherence Index, addressing the current absence of a standardised, robust measure of long-term medication adherence.

Using GP electronic health records, the AI Centre evaluated the index across three major classes of long-term medications and validated it against both subjective and objective adherence indicators. Results demonstrated strong predictive performance, underscoring the index’s potential to support medicines optimisation, risk stratification, and population health management.

National Deployment of OncoLlama

The AI Centre additionally highlighted progress on OncoLlama, an NHS-developed large language model designed to extract detailed Oncology information from unstructured clinical text.

Poor availability of structured cancer data has long constrained research, innovation, and clinical trial readiness. Through a national collaboration, the programme will deploy secure infrastructure within NHS Trusts, implement advanced LLM processing pipelines, and generate high-resolution Oncology datasets. The initiative aims to enhance real-world evidence generation, improve cancer registry data quality, and support clinical trial recruitment.

This work reflects the contributions of AI Centre team:

Alexandre Triay Bagur, Rafael Garcia-Dias, Virginia Fernández González, Jasjot Saund, Dan Stein, Isobel Weinberg, Lawrence Adams, Joe Zhang, Emily Jin, and Martin Chapman.

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