An autonomous AI agent for knowledge and data cooperation in ED clinical decision support

Medical knowledge accumulation and clinical practice form a closed loop, yet enabling effective cooperation between the two elements, namely autonomously distilling updated knowledge from dynamic data to guide practice, remains challenging, especially in the emergency department (ED). To overcome this, we developed an autonomous AI agent that integrates established medical knowledge graphs with dynamic clinical data into a hybrid graph of over 800,000 nodes. Using large language models (LLMs) for knowledge extraction and semantic mapping, the system dynamically selects the most relevant graph to power specialized tools for ED recognition, prediction, and decision-making. The agent achieves average improvements over state-of-the-art baselines of 23.13% in ED triage, 13.05% in drug-drug interaction detection, 1.58% in readmission prediction, and 5.47% in medication recommendation, demonstrating superior performance across all task categories. This demonstrates an effective framework for synergizing established medical knowledge and dynamic clinical data in emergency care.