Artificial Intelligence and Machine Learning in Hospital Quality Management, Patient Safety, and Accreditation Readiness: A Systematic Review and Narrative Synthesis

As healthcare systems become increasingly data-driven, artificial intelligence (AI) and machine learning (ML) are being explored as tools to support quality management, patient safety, and accreditation. This systematic review synthesizes the available evidence across these domains through a structured literature search of major biomedical databases, encompassing studies published over the past decade and narratively synthesized to capture implementation context alongside technical performance. Three electronic databases were searched, and 23 studies were included. The available empirical evidence suggests that these technologies are most useful for repetitive, operationally well-defined, and digitally captured tasks, while local validation, acceptance testing, and continuous monitoring consistently emerge as prerequisites for safe institutional deployment. Governance capacity and organizational readiness are identified as equally determinative of outcomes as algorithmic performance itself. Collectively, the evidence supports positioning AI and ML as augmentative instruments within human-led quality governance structures, rather than as autonomous operational systems. Evidence was heterogeneous, with most empirical studies being single-center, retrospective, or implementation-focused. The risk-of-bias assessment indicated limited high-quality comparative evidence, limiting the strength of the pooled conclusions.