<![CDATA[Anesthesiology and emergency care require rapid and accurate clinical decision-making. Artificial intelligence (AI) offers substantial potential to support triage, monitoring, and decision-making in critical and emergency anesthesiology settings. This scoping review maps the use of AI in clinical decision-making and emergency patient management in anesthesiology and identifies existing research gaps. A literature search was conducted in ScienceDirect, PubMed, Cochrane Library, and Google Scholar for articles in Indonesian or English published between 2020 and 2025. Study selection followed Tricco’s scoping review framework, and methodological quality was assessed using Joanna Briggs Institute (JBI) tools. Ten articles met the inclusion criteria. AI was shown to improve triage accuracy and efficiency (predictive accuracy up to 99.1% and reductions in waiting time of around 30%). Machine learning models effectively predicted critical care needs and emergency risk, while AI-based clinical decision support systems (CDSS) enhanced the speed and quality of clinical decisions. Key challenges include data bias, ethical and privacy issues, clinician readiness, and integration with hospital information systems. AI and CDSS have strong potential to improve patient safety and clinical decision-making in emergency anesthesiology. Strengthening AI literacy, supportive regulation, and transparent, context-appropriate predictive models are needed for safe and sustainable implementation.]]>
