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The integration of artificial intelligence (AI) into healthcare systems has transformed clinical analytics, particularly in improving medication safety through advanced reconciliation processes, structured error taxonomies, and careful deployment strategies. This narrative review examines how AI-driven analytics embedded within clinical infrastructures can reduce medication-related risks in hospital and ambulatory care settings. AI technologies such as natural language processing and machine learning enable automated detection of medication discrepancies by analyzing electronic health records and identifying inconsistencies that may be overlooked by manual review.AI systems also support the classification and prediction of medication errors, including prescribing mismatches and administration failures, allowing clinical decision support systems to identify high-risk prescriptions and support safer prescribing practices. In addition, AI contributes to closed-loop healthcare systems where analytics provide real-time decision support across the data lifecycle, from information ingestion to post-intervention feedback.Despite these benefits, several deployment constraints remain, including data quality limitations, interoperability challenges, and ethical concerns related to bias and governance. These factors highlight the importance of robust system design and transparent AI models to ensure safe and equitable implementation. Furthermore, AI can support standardized error taxonomies and pharmacovigilance through structured analytical frameworks that improve reporting and monitoring of adverse events.Overall, this review positions AI as a central component of adaptive clinical systems capable of strengthening medication safety. However, its effectiveness depends on addressing technical, operational, and regulatory barriers. Continued interdisciplinary collaboration will be essential to refine AI-enabled clinical analytics and support safer, more efficient healthcare systems.

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.