Federated AI Framework for Privacy-Preserving Differential Diagnosis Across Distributed Medical Networks

Abstract

In the modern healthcare, accurate and timely differential diagnosis is vital, and the scope is broad, in geographically distributed hospitals and clinics. Nevertheless, centralized AI training is sometimes constrained by data privacy regulations, as well as by soloed health information systems. This work presents a new Federated AI Framework that allows the privacy preserving differential diagnosis across distributed medical networks. It is a federation of learning (Federated learning), transformer based natural language processing (NLP) and explainable AI (XAI) techniques, as well as knowledge graph based reasoning that collaboratively trains AI diagnostic models on decentralized, sensitive patient data. The system uses patients' clinical notes, symptom data, and Electronic Health Records (EHRs) that it does not collect in a central server, keeping patient confidential. Local models are trained using participating medical institutions and aggregated periodically in a secure model update into a global model. Diagnostic predictions are dynamically refined through hierarchical disease ontologies and contextual feedback within an intelligent agent layer. Additionally, XAI techniques including SHAP and counterfactual analysis are also included in this framework for transparency of the model and trust by the clinicians. Gaining favourable diagnostic precision and recall while strictly keeping data privacy is demonstrated through experimental evaluation on multiinstitutional datasets. Not only does the proposed approach make advisable the reduction of diagnostic errors, but it also provides scalable, secure, and interpretable AI-driven healthcare solutions for real world deployment to privacy sensitive environments.

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