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The integration of artificial intelligence (AI) into healthcare systems has revolutionized clinical analytics, enabling predictive modeling, diagnostic support, and personalized interventions. However, the post-deployment phase of these AI systems presents unique challenges, particularly in maintaining performance amid evolving clinical environments. This narrative review synthesizes recent literature on post-deployment monitoring strategies for clinical AI, focusing on drift detection, feedback governance, and update policies within healthcare systems and analytics frameworks. We examine how data shifts—arising from changes in patient demographics, clinical protocols, or external factors—can degrade AI model efficacy, leading to suboptimal outcomes in high-stakes settings like disease prediction and resource allocation. Drift detection emerges as a cornerstone, encompassing statistical methods to identify concept drift, covariate shift, and label drift in real-time healthcare data streams. Techniques such as nonparametric monitoring and ensemble-based approaches allow for proactive identification of performance decay, ensuring AI systems remain aligned with dynamic clinical realities. Feedback governance integrates human-in-the-loop mechanisms, where clinician inputs refine AI outputs, fostering trust and regulatory compliance in governance structures. Update policies, including retraining schedules and federated learning paradigms, to address the need for iterative model evolution without disrupting clinical workflows. We highlight systems-level perspectives, such as closed-loop architectures that link monitoring to automated updates, emphasizing interoperability across electronic health records (EHRs) and AI pipelines. Comparative analysis reveals gaps in current practices, including limited scalability in resource-constrained settings and ethical considerations in data privacy during monitoring. Through an original synthesis, we propose an integrative framework for AI lifecycle management in healthcare, underscoring the interplay between drift metrics, governance protocols, and policy-driven updates to enhance patient safety and system resilience. This review underscores the imperative for standardized monitoring protocols, informed by multidisciplinary insights, to bridge the translational gap from AI development to sustained clinical utility. By addressing these elements, healthcare AI can achieve robust, adaptive performance, ultimately improving analytics-driven decision-making and outcomes in diverse clinical contexts. Future directions include harmonizing international guidelines for AI monitoring, integrating explainable AI for better feedback loops, and leveraging emerging technologies like edge computing for real-time drift management. This synthesis provides a foundation for researchers and practitioners to advance post-deployment strategies, ensuring AI’s enduring impact on healthcare systems.

Wearable and mobile sensing technologies are transforming healthcare by enabling continuous monitoring, real-time analytics, and personalized interventions. This narrative review explores recent advances in artificial intelligence (AI)–driven healthcare analytics, focusing on validation frameworks, drift detection, and generalization challenges associated with wearable sensing systems. Modern wearable devices equipped with biosensors capture physiological signals such as heart rate, activity, and stress indicators, while AI algorithms analyze multimodal data to generate actionable clinical insights. Ensuring reliability requires robust validation strategies that address sensor accuracy, data integrity, and clinical relevance in real-world settings. Drift detection methods help maintain model performance despite environmental changes and user variability. At the same time, generalization techniques support reliable deployment across diverse populations and clinical contexts, advancing scalable and adaptive digital healthcare systems.