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The integration of health data across organizational boundaries represents a cornerstone of modern artificial intelligence (AI) applications in healthcare systems and analytics, enabling enhanced predictive modeling, population health management, and personalized interventions. This narrative review synthesizes methodological approaches for cross-organizational data linkage, elucidates pathways through which biases emerge in these processes, and delineates validation standards essential for ensuring reliability and equity in AI-driven healthcare infrastructures. Drawing from literature, we examine how federated learning paradigms facilitate collaborative analytics without direct data sharing, thereby addressing privacy concerns while enabling multi-institutional model training. Approaches such as swarm learning and secure multi-party computation allow for distributed computation on decentralized datasets, mitigating risks associated with centralized repositories. However, such linkages introduce bias pathways, including selection biases arising from heterogeneous data sources, algorithmic amplification of disparities, and confounding factors rooted in demographic underrepresentation. For instance, racial and gender biases embedded in training data can propagate through linked systems, potentially leading to inequitable clinical outcomes. Validation standards are therefore critical to address these challenges, encompassing probabilistic linkage accuracy assessments, privacy-preserving evaluation metrics, and ethical frameworks designed to support fairness auditing. The review also highlights the potential role of blockchain technologies in enabling auditable linkage mechanisms and emphasizes the need for consensus-driven guidelines to standardize validation practices across healthcare ecosystems. In addition, the review integrates systems-level perspectives by framing data linkage as a foundational component of intelligent clinical decision support and closed-loop healthcare systems, where AI-driven analytics inform real-time interventions supported by continuous feedback mechanisms. Through this synthesis, the article underscores the importance of robust and bias-aware linkage methodologies for advancing AI-enabled healthcare analytics. Ultimately, the adoption of rigorous validation protocols can support trustworthy cross-organizational collaborations, reduce disparities, and enhance system resilience across diverse clinical environments. This work positions cross-organizational data linkage as a critical infrastructure for scalable AI healthcare applications and calls for interdisciplinary efforts to align methodological innovation with responsible ethical governance.

In the era of digital health transformation, the integration of patient data across disparate registries poses significant challenges to privacy and security, while enabling advanced artificial intelligence (AI) applications in healthcare systems and analytics. This narrative review synthesizes peer-reviewed literature to propose a principled framework for privacy-preserving patient identity resolution in multi-source record linkage. Drawing on advancements in federated learning, homomorphic encryption, and secure multiparty computation, the framework addresses the core tension between data utility for AI-driven clinical analytics and the imperative to safeguard patient confidentiality. We examine how AI techniques facilitate secure linkage of electronic health records (EHRs) without centralized data aggregation, enabling distributed analytics for precision medicine, population health monitoring, and real-time decision support. Key systems-level considerations include architectural designs that incorporate differential privacy mechanisms to mitigate re-identification risks during identity matching processes, such as probabilistic record linkage enhanced by machine learning models. The review highlights integrative approaches where AI models operate on encrypted data silos, preserving linkage accuracy while complying with regulatory standards like HIPAA and GDPR. For instance, multiparty homomorphic encryption allows collaborative identity resolution across registries without exposing raw identifiers, supporting analytics pipelines for disease outbreak tracking and personalized treatment pathways. We discuss closed-loop healthcare systems where resolved identities feed into AI analytics for predictive modeling, such as inferring multimodal latent topics from EHRs to inform clinical outcomes. The framework emphasizes governance layers, including ethical oversight for algorithmic fairness in linkage processes that could exacerbate health disparities. By structuring the synthesis around data ingestion, secure linkage, AI inference, and feedback loops, this review positions privacy-preserving identity resolution as a foundational enabler for scalable AI in healthcare infrastructure. It underscores the need for interdisciplinary integration of computational techniques with clinical workflows to achieve equitable, secure multi-source data utilization. Ultimately, the proposed framework offers a roadmap for deploying AI systems that balance innovation in healthcare analytics with robust privacy protections, fostering trust in digital health ecosystems.

Acute kidney injury (AKI) is a common and serious condition in critical care, making early prediction essential for timely intervention, reduced mortality, and lower healthcare costs. Machine learning methods using electronic health records have shown promise in identifying at-risk patients, but their performance is often limited by reliance on single-institution datasets and poor generalizability across populations. Privacy regulations such as HIPAA and GDPR further restrict cross-hospital data sharing, hindering the development of more robust models.To address these challenges, this study proposes a federated learning–based framework for AKI prediction, enabling multiple hospitals to collaboratively train models without exchanging raw patient data. Each institution acts as a local client that trains on its own data and shares only model updates, which are aggregated into a global model. The framework incorporates standardized feature processing, secure aggregation, and communication-efficient strategies to ensure scalability across heterogeneous healthcare environments.This privacy-preserving approach improves model generalization by leveraging diverse multi-institutional data while maintaining regulatory compliance. Although it introduces challenges such as communication overhead and convergence complexity, these are mitigated through optimized aggregation methods. Overall, the proposed framework enhances predictive performance, supports clinical decision-making, and offers a scalable foundation for future privacy-aware healthcare AI systems in AKI management.