ARTIFICIAL INTELLIGENCE IN INTRA-HOSPITAL RISK MANAGEMENT RELATED TO PATIENT SAFETY

Abstract

Introduction: The complexity of hospital organizations and the social, economic, and psychological impact of adverse events (AEs) make patient safety an essential priority for healthcare quality. Patient safety is understood as the reduction of unnecessary harm associated with healthcare to an acceptable minimum. Objective: To evaluate, through a scoping review, the role of Artificial Intelligence in Hospital Risk Management related to patient safety. Methodology: This is a scoping review based on the methodological recommendations of the Joanna Briggs Institute (JBI). The survey was conducted between March and May 2026 across the PubMed, Scopus, SciELO, and BDTD databases, as well as Ministry of Health/IBICT manuals, along with an additional search on Google Scholar (grey literature). Primary quantitative and qualitative studies, theses, dissertations, and technical documents published within the last 10 years (in English, Portuguese, and Spanish) were included.Results and Discussion: The findings were correlated with the recent regulatory outlook, highlighting the publication of Ordinance GM/MS No. 10,220 in February 2026, which established the Risk Management Policy within the Ministry of Health to support secure institutional planning. Despite the benefits, the literature highlights severe obstacles to consolidating AI technologies in healthcare, such as the "black box" dilemma, data biases, hallucinations, and technical flaws, as well as structural barriers and challenges in professional training. Final Considerations: The study concludes that Artificial Intelligence plays a transformative and highly beneficial role in healthcare when utilized as a "second layer of protection" for professionals, without ever replacing human clinical judgment and reasoning.