Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle...

No prior work has resolved how fragmented literature on post-development clinical artificial intelligence can be unified. Prior research has shown that individual studies often focus on narrow aspects like drift or human interaction. This gap motivated a comprehensive synthesis of existing review-level evidence. It was already known that clinical tools require more than just initial validation for long-term reliability. That uncertainty drove the need to map how governance and monitoring are currently conceptualized. Previous efforts remained siloed within specific academic traditions. No single framework currently bridges the divide between technical performance and organizational readiness. This synthesis addresses the disconnect between theoretical requirements and practical implementation in routine care.

The aim of this study is to synthesize existing review-level literature on the post-development management of clinical artificial intelligence. This work addresses the fragmentation of current knowledge across different research traditions. The authors seek to clarify how monitoring, implementation, and governance are conceptualized. This gap motivated a systematic mapping of the current evidence base. The researchers intended to distinguish between operational and conceptual requirements for long-term system reliability. They aimed to identify the persistent normative-operational gap in the field. This study provides a comprehensive overview of the challenges facing systems in routine care. The findings serve to guide future research toward more practical, actionable governance models.

The review approach involved searching MEDLINE, Embase, Scopus, and Web of Science Core Collection. Investigators covered all records from database inception through February 2026. This review approach focused exclusively on review-level and review-oriented publications. The team charted characteristics across all included documents. An inductive thematic synthesis allowed for the categorization of findings. Authors distinguished between operational, deployment-proximal, methodological, and conceptual evidence. This review approach ensured a broad synthesis of diverse academic traditions. The methodology prioritized the extraction of findings related to post-development system management.

Key findings from the literature indicate that twenty-five review-level publications met the inclusion criteria. Clinically important risks were consistently framed as socio-technical challenges. Trustworthiness depended on fairness, transparency, workflow fit, and organizational readiness. The literature consistently recommended post-deployment monitoring but showed limited operational maturity. Methods for monitoring, action thresholds, and fairness surveillance were weakly standardized. Mature evidence from activated systems in routine care remained sparse. Trustworthy implementation was increasingly framed as a lifecycle governance challenge. This includes local validation, subgroup auditing, drift detection, and incident response.

The authors propose that trustworthiness must be viewed as a continuous lifecycle property rather than a static achievement. Future efforts should prioritize the generation of robust operational evidence. Clearer reporting standards for deployment-proximal evaluations are required to improve transparency. Methodological standardization of monitoring metrics and action thresholds remains a high priority. Researchers should focus on implementation science to develop feasible governance models. Evaluation frameworks must incorporate safety, fairness, and accountability metrics. Sustainability assessments are necessary to ensure long-term system viability. These steps will help bridge the current gap between normative expectations and real-world operational reality.