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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.