Federated Explainable AI Framework for Cross-Domain Medical Image Analysis

FedAD: adaptive federated disentanglement for domain generalization in unlabeled medical imaging

FedAD: Adaptive Federated Disentanglement is presented, a unique framework that uses two key ideas to handle problems in a synergistic way in terms of generalization to unseen target domains, and outperforms current approaches in terms of generalization to unseen target domains.

Regulatory-orientedDeep federated learning framework for multi-hospital medical imaging: privacy-preserving, explainable, and generalizable diagnosis

The proposed FL framework provides a privacy-preserving, explainable, and computationally efficient solution for collaborative AI in medical imaging by combining adaptive federated learning, secure privacy mechanisms, and explainable AI techniques, demonstrating strong potential for deployment in multi-hospital clinical environments.

Federated learning for medical image classification based on prototype alignment

Interpretable deep learning framework for secure clinical data analytics

The findings indicate the effectiveness of the suggested strategy in providing secure, transparent, and reliable clinical decision support, rendering it an appropriate strategy to implement in contemporary healthcare systems.

Fed-LoRA: Cross-Domain Generative Knowledge Feedback for Few-Shot Medical Anomaly Detection

Deep learning in medical imaging is severely hindered by the "domain gap," where significant imaging discrepancies exist even among images of the same modality and anatomical structure due to heterogeneous scanning devices and diverse clinical environments. While major medical centers possess high-fidelity data, resource-constrained clinics often struggle with legacy hardware and severe data scarcity. This imbalance leads to a significant collapse in diagnostic performance and poor model generalization when deployed in low-resource settings. To address these challenges, we propose a novel few-shot Federated Low-Rank Adaptation (Fed-LoRA) framework for cross-domain generative feedback. By leveraging the lightweight nature of LoRA, our method enables the efficient transmission of global pathological priors from a high-resource Hub to decentralized Edge nodes with minimal communication overhead. Crucially, this mechanism facilitates cross-domain data generation while ensuring privacy by precluding the transmission of raw medical data. This allows local models to synthesize high-fidelity abnormal exemplars precisely tailored to specific institutional styles. Extensive experiments demonstrate that our synthetic images significantly enhance the performance of downstream anomaly detection tasks. This work provides a scalable and secure solution for mitigating data scarcity and achieving effective cross-domain adaptation in decentralized healthcare systems.

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 image interpretation, vision transformers for modeling long-range dependencies, and generative adversarial networks for image reconstruction and synthesis. The review further discusses the emergence of multimodal foundation models that integrate imaging with textual and genomic data to enhance diagnostic robustness. The application of these technologies is analyzed across three primary domains: Radiology (image enhancement and automated interpretation), cardiology (electrocardiographic and echocardiography analysis), and oncology (tumor classification and treatment planning). Specific attention is given to the national context in Türkiye, highlighting local initiatives such as TEKNOFEST and TÜBİTAK-supported projects that foster domestic AI development. While AI offers significant benefits in terms of diagnostic accuracy and treatment workflow optimization, challenges regarding data privacy, algorithmic bias, and interpretability (“black box” issues) persist. Future progress depends on the development of explainable AI, rigorous prospective validation, and the establishment of ethical regulatory frameworks.