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Federated learning (FL) has emerged as a transformative paradigm in artificial intelligence (AI) for healthcare systems and analytics, enabling collaborative model training across distributed institutions without direct data sharing, thereby addressing stringent privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR). This narrative review synthesizes the architectural models underpinning FL ecosystems in healthcare, elucidating their integration into clinical analytics pipelines and the privacy trade-offs they entail. We delineate how FL facilitates decentralized AI applications in areas such as predictive modeling for clinical outcomes, medical imaging analysis, and real-time health monitoring, while balancing model utility against data protection imperatives.Central to FL architectures are client-server frameworks where edge devices (e.g., hospitals or wearable sensors) perform local training on siloed datasets, aggregating updates via a central coordinator to refine global models. Variants include horizontal FL for identical feature spaces across institutions and vertical FL for complementary datasets, often augmented with differential privacy mechanisms to mitigate inference attacks. In healthcare systems, these models support analytics for disease prediction, as seen in COVID-19 outcome forecasting, and enable scalable infrastructures for multi-institutional collaborations without compromising patient confidentiality. However, privacy trade-offs manifest in reduced model accuracy due to noisy perturbations, communication overheads in bandwidth-constrained environments, and vulnerabilities to model inversion or membership inference attacks.We explore the landscape of AI-driven healthcare systems, highlighting how FL integrates with electronic health records (EHRs), imaging repositories, and wearable data streams to foster intelligent analytics. Key syntheses include closed-loop systems where AI inferences inform clinical decisions, feedback loops recalibrate models, and governance layers ensure ethical deployment. Challenges such as data heterogeneity across federated nodes and the need for robust incentive mechanisms are critically examined, alongside opportunities for hybrid FL-blockchain integrations to enhance trust. This review posits that optimized FL ecosystems can revolutionize healthcare delivery by enabling privacy-preserving, generalizable AI analytics, but that these systems require interdisciplinary frameworks to navigate trade-offs between innovation and patient safeguards. Ultimately, FL represents a cornerstone for sustainable, equitable AI in healthcare, promoting data sovereignty while accelerating clinical insights.

Foundation models, characterized by their large-scale pretraining on diverse datasets, represent a transformative paradigm in artificial intelligence (AI) applications for healthcare systems and analytics. These models, often based on transformer architectures, enable generalist capabilities that extend beyond narrow task-specific AI, facilitating integration into complex healthcare infrastructures. This review synthesizes recent literature on the architectural integration of foundation models into healthcare systems, emphasizing their role in enhancing clinical analytics, decision support, and operational efficiency while addressing critical oversight considerations, including ethical, regulatory, and safety frameworks.In healthcare systems, foundation models are increasingly deployed to process multimodal data streams, including electronic health records (EHRs), medical imaging, and real-time patient monitoring. Architectural integration involves embedding these models within hospital information systems, enabling seamless data ingestion, inference, and feedback loops. For instance, models like those adapted from large language models (LLMs) support natural language processing for EHR mining, predictive analytics for disease progression, and generative tasks for synthetic data augmentation. Oversight considerations are paramount, encompassing regulatory compliance, bias mitigation, and human-AI collaboration protocols to ensure patient safety and equity.The synthesis highlights key architectural patterns: federated learning for privacy-preserving model training, hybrid human-AI workflows for clinical decision-making, and adaptive systems for continuous model recalibration. Analytics applications span precision medicine, where foundation models integrate genomic and clinical data for personalized interventions, to population health management, optimizing resource allocation through predictive modeling. Ethical oversight includes checklists for AI deployment in low- and middle-income countries (LMICs), emphasizing equitable access and cultural adaptability.Challenges in integration include data interoperability, model interpretability, and scalability in resource-constrained settings. Regulatory imperatives call for validation frameworks and safety standards to govern the rollout of generative AI. This review provides an original systems-level framing, structuring the discourse around data-to-decision pipelines, governance overlays, and evaluative metrics for sustainable adoption.Ultimately, foundation models hold promise for closed-loop healthcare systems, where AI-driven insights inform interventions and feedback refines models iteratively. However, rigorous oversight is essential to balance innovation with accountability, ensuring these technologies augment rather than disrupt clinical workflows. By synthesizing high-impact publications, this narrative review offers integrative insights for researchers, clinicians, and policymakers navigating AI-enabled healthcare transformation.

Sepsis prediction models in intensive care units often degrade over time due to changes in clinical practice, patient populations, and data recording processes, a phenomenon known as model drift that can compromise patient safety. Traditional federated learning approaches are not well-suited to these evolving conditions, as they assume static data distributions and typically require costly retraining that risks forgetting previously learned knowledge, while also being constrained by privacy limitations that prevent central data pooling. To address these challenges, this paper proposes a federated continual learning framework that enables ongoing, privacy-preserving model adaptation across multiple hospitals without catastrophic forgetting. The framework integrates local continual learning methods (such as elastic weight consolidation or memory replay) with federated aggregation and importance-weighted parameter updates to support continuous learning from new clinical data while preserving prior knowledge. This design allows each institution to adapt models to local data shifts while collaboratively improving a shared global model without sharing patient-level data. Overall, the proposed approach offers a scalable solution for maintaining robust, adaptive sepsis prediction systems in dynamic healthcare environments, reducing the need for repeated full retraining and supporting long-term clinical deployment.