SOURCE-LINKED INTELLIGENCE

OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis

arXiv · AI, language, vision and robotics · article · Sep 9, 2026 · UTC

Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms cannot analyze these data types together. Meanwhile, compliance with HIPAA and GDPR can constrain centralized aggregation of sensitive patient data. This leaves a crucial void of secure fusion of visual and textual context across distant networks. Thus, we present OmniMed-FL, a controlled systems study of multimodal federated learning for five-class clinical condition classification (Normal, Pneumonia, COVID-19, Pleural Effusion, Cardiomegaly). O

Read original source ↗ Open in workspace

recordType

paper

region

Global