60 Post-deployment artificial intelligence model monitoring, evaluation, and intervention in health systems: A scoping review for guidelines for AI model report and the literature

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Objectives/Goals: This scoping review aims to synthesize current literature on post-deployment monitoring of AI-enabled digital health solutions within clinical practice. Findings identify existing approaches and gaps that inform guidance for post-deployment monitoring in clinical practice. Methods/Study Population: We conducted a scoping review in accordance with PRISMA-ScR guidelines to characterize the current landscape of post-deployment monitoring in healthcare systems. A PubMed search targeted peer-reviewed articles in English published between 2015 and mid-2025, including text or MeSH terms on 1) health system/hospital; 2) artificial intelligence; 3) post-deployment; and 4) evaluation/monitoring. We performed a thematic analysis to identify common challenges, gaps, and opportunities in AI oversight. Additionally, we reviewed guidelines addressing post-deployment AI monitoring. All analyses were conducted using Rayyan.ai and Microsoft Word. Results/Anticipated Results: Among the six studies included after the full-text review, five provide recommendations to ensure transparency, safety, and model performance. These recommendations encompassed monitoring model performance and real-time case report, post-market surveillance, adverse event reporting, end-user training, data standardization and documentation, and interdisciplinary collaboration. One study reports a framework for post-deployment impact grading. Currently, no guidelines addressing post-deployment of AI monitoring in health systems exist. Our findings highlight the urgent need for structured post-deployment processes to ensure AI in healthcare systems is safe, effective, and trustworthy. Discussion/Significance of Impact: The absence of post-deployment guidelines raises concerns. This review underscores the need for interdisciplinary collaboration to establish a post-deployment monitoring process with scientific rigor, scalability, and sustainability that aligns with operational realities.

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