🧠 Predicting delirium in emergency critical care is a major hurdle. A new AI system called DeLiriuMAgents is changing that by simulating multidisciplinary clinical consultations. 🏥 Published in Cell Reports Medicine, this multi-agent LLM framework combines data-driven prediction with clinical specialist reasoning to catch delirium risk early in the emergency department. How it works: 1. EHRPromptAgent: Translates tabular EHR data into clinical narratives. 2. ModelPhysicianAgent: Employs a machine learning model to estimate baseline risk. 3. Multidisciplinary Specialists: Emergency, neurology, and psychiatry agents provide independent clinical reasoning. 4. MedEvidenceAgent: Pulls real-time guidelines and literature via Retrieval-Augmented Generation to reduce hallucinations. 5. DeliriumDecisionAgent: Coordinates all inputs to deliver an explainable, evidence-backed risk report. 📈 Performance and Validation: - Validated across MIMIC-IV, eICU-CRD, and a Chinese Peking University cohort. - Achieved predictive accuracies of 74.9% on MIMIC-IV, 73.1% on Peking University, and 67.0% on eICU-CRD. - Clinicians aided by these AI reports saw diagnostic accuracy rise from 62.0% to 65.0%. - 96% of clinicians surveyed are willing to use the system in real-world practice. 🧑‍⚕️ By shifting from post-onset treatment to early ED-ICU prediction, DeLiriuMAgents provides clinicians with the warnings they need to start early prevention. AMA Reference: Shang W, Shi T, Ma Q, Kong G. A large language model-driven multidisciplinary AI agent system predicts delirium in emergency critically ill patients. Cell Rep Med. 2026;7:102986. Article link: doi.org/10.1016/j.xcrm.2026.… @anirbanb_007 @STRXLabs