A Comprehensive Literature Review on AI-Assisted Multimodal Triage Systems for Health Centers

Artificial intelligence (AI) is increasingly recognized as a transformative tool in emergency department (ED) triage. Traditional triage methods, such as the Emergency Severity Index (ESI) and the Canadian Triage and Acuity Scale (CTAS), prioritize patient care based on acuity but face challenges, including subjectivity, overcrowding, and inefficient resource allocation. AI offers enhanced predictive accuracy, optimized patient prioritization, and reduced human error. This review examines the evolution of triage systems from conventional to AI-assisted models, highlighting advancements and limitations of AI in clinical practice. Recent findings underscore AI’s potential to improve diagnostic precision and streamline ED workflows. However, critical concerns include data dependency, ethical challenges, and variable performance across healthcare settings.