Background/Objectives: Clinical artificial intelligence (AI) is increasingly moving from proof-of-concept development into clinical evaluation, regulatory review, and routine care. This scoping review aimed to map and synthesise empirical evidence on clinical AI evaluation after model development, focusing on deployment-related robustness, post-development monitoring, and lifecycle oversight in practice. Methods: We conducted a scoping review in accordance with Joanna Briggs Institute guidance and reported findings using PRISMA-ScR. MEDLINE, Embase, Scopus, and Web of Science Core Collection were searched with no lower date restriction within each database’s available indexed coverage and with a common upper search date of 28 February 2026. Searches were supplemented by backward and forward citation tracking. Grey literature, preprint servers, and regulatory databases were not systematically searched because eligibility was restricted to full-text, peer-reviewed empirical studies and empirically grounded implementation or monitoring reports. Findings were synthesised using descriptive evidence mapping and inductive thematic synthesis. Results: Eighteen studies or empirically grounded reports were included. Evidence was organised into five strata: direct live or post-deployment monitoring studies; near-live bridge studies generating prospective outputs without guiding care; methodological monitoring and maintenance studies; deployment-relevant robustness and predeployment safety studies; and governance, implementation, readiness, and human-factors studies. Three themes emerged: trustworthiness after development was conditional and context-dependent; algorithmovigilance extended beyond aggregate performance tracking to include operational, workflow, fairness, contextual, and user-feedback signals; monitoring was more actionable when linked to corrective pathways, governance structures, and institutional readiness. Sociotechnical failures included automation-bias signals, workflow burden, reasoning–conclusion misalignment, and workflow-fit problems. Conclusions: Post-development clinical AI evaluation remains a layered and emerging field rather than a mature monitoring literature. Direct live evidence is limited, concentrated in high-income settings, and weighted towards radiology. The findings should be interpreted as synthesis-informed rather than as empirically validated standards for lifecycle oversight.

Background/Objectives: Nurses’ capacity to enact Lean-oriented hospital improvement is often conflated with organizational support. This review examines capability, context, implementation, sustainment, and outcomes. Methods: Targeted, non-exhaustive searches in PubMed and Scopus, supplemented by Google Scholar and citation chaining, were updated on 30 July 2026. Sixty empirical reports published during 2007–2026 were synthesized using interpretive comparison and claim-level appraisal, without quantitative pooling. Results: Three psychometric reports concerned two nursing-specific Lean instruments; no identified instrument directly validated the specific capability construct proposed here. We interpret practical knowledge and judgement, collective problem-solving, learning, professional agency, and enacted leadership as candidate manifestations requiring validation. Operational process changes were reported, but causal attribution was weak. Evidence for reallocated bedside time, net economic value, workforce or patient benefit, environmental benefit, and long-term sustainment was limited or inconsistent. Conclusions: The framework is a provisional interpretive map. Future evaluations should measure capability, context, implementation, sustainment, benefits, and harms separately.

Lean-oriented redesign is widely used to improve hospital flow and productivity, but the capabilities nurses need to implement and sustain change remain poorly defined. This critical narrative review integrates empirical evidence concerning nursing capability, organizational conditions, implementation, sustainment, and performance. Nursing-specific instruments assess perceived Lean implementation or service leanness, but none validates nursing improvement capability as an adaptive individual and collective capacity. The most credible candidate manifestations are practical knowledge and judgement, collective problem-solving, learning, professional agency, and enacted improvement leadership. Staffing, protected time, decision authority, data access, and infrastructure shape whether these capabilities can be expressed but are not equivalent measures of capability. Operational process improvements are reported most often, yet causal attribution is weak. Evidence for reallocated bedside time, net economic value, workforce benefit, patient safety, environmental benefit, and long-term sustainment remains limited or inconsistent. Evaluations should measure capability, context, implementation, sustainment, benefits, and harms separately and prospectively.