Federated learning for multi-institutional AI in healthcare via digital pathology

Federated Learning Pipeline for Whole-Slide Image Classification in Digital Neuropathology.

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

OmniMed-FL is presented, a controlled systems study of multimodal federated learning for five-class clinical condition classification (Normal, Pneumonia, COVID-19, Pleural Effusion, Cardiomegaly) and benchmarks eight fusion strategies, three initializations, four missing-text imputation rules, and matched federated bas...

FedSCF: sensitivity-aware collaborative fusion for personalized federated learning in medical image classification

A PFL framework, FedSCF, which models client heterogeneity at the parameter level, including a relative perturbation-based sensitivity evaluation is designed to identify critical parameters for personalized modeling, while the remaining parameters participate in cross-client sharing.

A Federated Artificial Intelligence Framework for Optimizing Pancreatic Cancer Treatment - Strategy Update

While a centralized approach involving patient consent to collect and analyze data centrally would theoretically offer the best data quality and predictive performance, it is not always feasible in practice. Federated Learning (FL) architectures have shown to be a very promising approach to use and access distributed d...

FedXAI-Health: Privacy-Protected Explainable Federated Deep Learning Model in Pre-emption of Early Diseases with Multimodal Healthcare Data

Machine learning (ML) has significantly accelerated healthcare analytics, but its widespread clinical implementation is hindered by issues of data privacy, inter-institutional heterogeneity, and a lack of interpretability. To overcome these challenges, this paper proposes FedXAI-Health, a novel framework that combines...

Artificial intelligence in healthcare: Technical advances, clinical integration, and future perspectives

This narrative review examines the evolution of artificial intelligence (AI) in healthcare, with a focus on the transition from early rule-based systems to modern deep learning architectures and their integration into clinical practice. We examine foundational technologies, including convolutional neural networks for i...