Medical Distributed Operating System: A Federated AI Framework for Fragmented Healthcare Data

Background & Context:
Modern healthcare infrastructure is critically fragmented. Patient data reside across disparate systems, formats, and physical locations, hindering comprehensive clinical analysis. The urgent challenge is to develop a Medical Distributed Operating System that securely bridges these silos. Without centralising sensitive patient records, there is a profound need for privacy-preserving AI architectures capable of interrogating distributed data to support predictive decision-making.

Objectives & Hypothesis:
This project hypothesises that a decentralised AI framework can achieve predictive parity with centralised models while strictly preserving institutional data sovereignty. The objectives are to: (1) design a federated, interoperable Medical Distributed Operating System; (2) develop AI algorithms capable of interrogating heterogeneous clinical databases in situ; and (3) establish robust cryptographic standards for secure cross-domain model training.

Methodology:
The research will engineer a federated learning architecture enabling machine learning models to operate across distributed healthcare nodes without extracting raw data. Heterogeneous inputs—including electronic health records, imaging, and unstructured clinical notes—will be standardised using semantic interoperability protocols such as HL7 FHIR. The framework will integrate secure multiparty computation and differential privacy to safeguard patient information during the aggregation of local model parameter updates. Performance will be evaluated across simulated multi-site NHS environments, rigorously benchmarking the system’s scalability, latency, and predictive accuracy against traditional centralised data lakes to validate its robustness under domain shift. Gloucestershire Hospitals NHS Trust will provide an honorary contract for the duration of the project, in accordance with the collaboration agreement between the University and the NHS Trust.

Expected Impact & Outcomes:
This architecture will fundamentally transform healthcare AI by enabling secure, multi-institutional collaboration without compromising patient privacy or data ownership. Expected outcomes include a highly scalable prototype of the Medical Distributed Operating System and impactful publications in health informatics. Aligning with Radiance programmatic goals, this research will accelerate the ethical deployment of advanced diagnostic and operational tools across fragmented health networks, establishing a new scientific paradigm for distributed data utilisation in modern medicine.