It is discussed how FL can enable scalable, privacy-preserving AI models, and future directions for standardizing and validating FL-based approaches in clinical workflows are outlined.
Abstract
Abstract The integration of artificial intelligence (AI) in digital pathology has shown significant promise in advancing cancer diagnostics, grading, and treatment response prediction. However, widespread development and deployment of robust AI models face critical challenges due to data silos, privacy concerns, and the need for large-scale multi-institutional datasets. Federated Learning (FL) presents a transformative approach by enabling collaborative model training across hospitals without direct data sharing. In this review, we summarize recent developments in FL as applied to digital pathology, highlight pioneering use cases, and explore the technical, regulatory, and ethical hurdles. We discuss how FL can enable scalable, privacy-preserving AI models, and outline future directions for standardizing and validating FL-based approaches in clinical workflows.
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...
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.
Mingjun Wei, Rongyang Xu, Qian Zhang et al.· Engineering Research Express· 0 citations
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...
Anne-Christin Hauschild, Amirreza Aleyasin, N. Beyer et al.· 0 citations
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...
Kaljot Sharmaa, Neetu Rania· Innovative Trends in Multidi...· 0 citations
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...
Abdulkadir Yıldırım, Ö. Özdemi̇r· Artificial Intelligence in M...· 0 citations
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
