From Algorithmic Performance to Hospital Value: A Narrative Review of Clinical Artificial Intelligence

Clinical artificial intelligence (AI) has demonstrated performance across diagnostic, predictive, monitoring, and decision-support tasks, yet hospital adoption depends on whether that capability can produce sustainable value under local conditions. Informed by structured searches of PubMed/MEDLINE and Scopus, supplemented by Google Scholar and citation tracking (2015–July 2026), this narrative review synthesizes evidence on clinical AI methods, applications, implementation, evaluation, and sustainability in hospitals. Evidence remains uneven: image-based applications have substantial evidence for diagnostic performance, whereas prospective clinical utility, workflow effects, economic consequences, and organizational sustainability are evaluated less consistently. Established approaches—including efficacy hierarchies, the Radiology AI Deployment and Assessment Rubric (RADAR), hospital-based health technology assessment, implementation science, and AI governance frameworks—offer perspectives on performance, transferability, implementation, and value. Building on these traditions, this review proposes institutional efficacy as the warranted and revisable hospital-level judgment that an AI-enabled service configuration can generate and sustain patient-centered value under local priorities, constraints, and adaptive capacity. The review further proposes that deeper organizational integration increases the proportion of realized-value evidence requiring local generation or verification. Two additional properties—context and data dependence, and the consequence profile—separately shape that burden. External evidence remains essential, but hospital-specific appraisal is necessary to determine whether value can be realized and sustained.