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Care pathways represent the temporal sequences of clinical events that define real-world patient journeys within complex healthcare systems. Recent advances in artificial intelligence have enabled the analysis of these pathways through sequence analytics, uncovering latent patterns beyond traditional guideline-based approaches. This narrative review synthesizes literature to examine three pillars of AI-enabled care pathway analytics: clustering methods that group similar patient trajectories, deviation detection techniques that identify meaningful variations from expected flows, and interpretability frameworks that support transparency and clinician trust.Drawing on process mining, sequence analysis, and explainable AI, the review highlights how electronic health record data can be transformed into actionable insights for clinical decision-making. Clustering approaches reveal hidden patient subgroups across domains such as oncology, cardiology, mental health, and critical care. Deviation detection methods expose bottlenecks, workarounds, and non-adherence associated with adverse outcomes and inefficiencies. Interpretability frameworks link algorithmic outputs to clinical logic, improving trust and adoption in healthcare settings.Cross-study evidence shows that while clustering and deviation detection methods have advanced significantly, their integration with interpretability remains limited, constraining large-scale implementation. The review proposes an integrative systems perspective that positions care pathway sequence analytics as a foundational component of AI-enabled healthcare infrastructure, encompassing data pipelines, model inference, intervention orchestration, and governance. Overall, AI-driven pathway analytics offers the potential to move healthcare from reactive, guideline-based care toward proactive, personalized, and continuously learning systems.

Distribution shifts pose a major challenge for artificial intelligence (AI) deployed in healthcare systems, as models trained on historical or controlled datasets often perform poorly in evolving clinical environments. This narrative review synthesizes current approaches for detecting, adapting to, and classifying failures associated with distribution shifts in AI-driven healthcare analytics. Sources of shift—including changes in patient demographics, imaging protocols, institutional practices, and temporal dynamics—can significantly affect clinical decision support, predictive modeling, and operational analytics.We examine detection strategies based on statistical divergence monitoring and discuss adaptation methods such as domain adaptation and privacy-preserving learning approaches designed to maintain model performance across institutions. Failure modes are organized into core categories, including covariate shift, label shift, and concept drift, with particular attention to healthcare-specific risks such as bias amplification and breakdowns in continuous monitoring systems.From a systems perspective, the review highlights the importance of integrating shift detection with clinical analytics pipelines, governance mechanisms, and explainable AI tools to support safe deployment. We propose an interpretive framework linking data ingestion, model inference, intervention feedback, and oversight processes within healthcare infrastructures. Despite advances in detection and adaptation techniques, real-time operational deployment and standardized failure classification remain significant gaps. Strengthening these areas is essential for developing resilient AI systems capable of maintaining reliability in dynamic healthcare environments.

The integration of artificial intelligence (AI) into healthcare has enhanced data-driven decision-making, but missing data remains a major barrier to reliable model performance. This narrative review synthesizes literature on missing data in clinical machine learning, focusing on modeling decisions, common pitfalls, and emerging reporting standards within AI-enabled healthcare systems.Missing data in healthcare arises from sources such as electronic health records (EHRs), wearable devices, and clinical trials, and may follow mechanisms including missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). Addressing these gaps requires appropriate imputation strategies, from statistical methods like multiple imputation to advanced deep learning approaches such as generative adversarial networks (GANs), each carrying implications for bias and model generalizability.This review highlights key challenges, including underreporting of missingness, insufficient sensitivity analyses, and neglect of imputation uncertainty. It also examines evolving reporting standards that emphasize transparency in missing data handling. By synthesizing cross-study evidence, the review proposes a systems-level framework for integrating missing data management into AI governance, supporting more reliable, transparent, and equitable healthcare analytics.