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Post-deployment performance degradation in clinical artificial intelligence systems remains a persistent barrier to sustained patient safety and regulatory adherence. Unlike pre-market validation, real-world deployment exposes models to continuous data shifts, input anomalies, and contextual drift that standard retraining protocols cannot preemptively address. This conceptual systems manuscript presents an original error-taxonomy framework designed specifically to identify, classify, and act upon post-deployment error signals, thereby triggering safe, targeted model revisions without disrupting clinical workflows. Synthesizing peer-reviewed evidence, the framework introduces a layered orchestration infrastructure that integrates error taxonomy classification with governance-constrained decision logic. A unique closed-loop feedback topology ensures iterative refinement while preserving traceability for auditability. Three interpretive formulas quantify risk propagation, decision confidence under taxonomic uncertainty, and governance load. The proposed architecture, termed the error taxonomy update and revision framework (ETURF), provides a theoretical blueprint for responsible lifecycle management across imaging, tabular, and multimodal clinical environments. By anchoring revision triggers to clinically interpretable error categories rather than aggregate metrics, the framework advances infrastructural safety in healthcare AI deployment. This work establishes a conceptual foundation for future integration into hospital information systems and regulatory oversight mechanisms.

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.