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Clinical AI Beyond Development: A Scoping Review of Deployment-Related Robustness, Algorithmovigilance, and Lifecycle
Rabie Adel El Arab1, Mohammad Hussein Mustafa2, Wesam Taher Almagharbeh3
1Almoosa College of Health Sciences, Al Ahsa 36422, Saudi Arabia.
Healthcare (Basel, Switzerland)
|July 28, 2026
Summary
Post-development clinical artificial intelligence (AI) evaluation is an emerging field. Trustworthiness and monitoring require context-specific approaches, linking to corrective actions for effective lifecycle oversight.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Health Services Research
Background:
- Clinical artificial intelligence (AI) is transitioning from development to routine care.
- Empirical evidence on post-development AI evaluation is crucial for safe and effective deployment.
- Existing research often focuses on pre-deployment validation rather than real-world performance.
Purpose of the Study:
- To map and synthesize empirical evidence on clinical AI evaluation after model development.
- To focus on deployment-related robustness, post-development monitoring, and lifecycle oversight.
- To identify themes and gaps in the practical evaluation of clinical AI.
Main Methods:
- Conducted a scoping review following Joanna Briggs Institute guidance and PRISMA-ScR.
- Searched major databases (MEDLINE, Embase, Scopus, Web of Science) without lower date restrictions.
- Synthesized findings using descriptive evidence mapping and inductive thematic synthesis.
Main Results:
- Included 18 studies/reports across five strata: live monitoring, near-live studies, methodological studies, robustness studies, and governance/human-factors studies.
- Trustworthiness of clinical AI is conditional and context-dependent.
- Algorithmovigilance encompasses performance, operational, workflow, fairness, contextual, and user-feedback signals, requiring linkage to corrective pathways and governance.
Conclusions:
- Post-development clinical AI evaluation is an emerging, layered field, not a mature monitoring literature.
- Direct live evidence is limited, concentrated in high-income settings, and primarily in radiology.
- Findings offer synthesis-informed insights, not empirically validated standards for AI lifecycle oversight.
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