[1]WANG Jing,SHEN Le,LIN Fei,et al.MorpheusAPI: an LLM Agent for intelligent anesthesia platform[J].CAAI Transactions on Intelligent Systems,2026,21(1):156-166.[doi:10.11992/tis.202505004]
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MorpheusAPI: an LLM Agent for intelligent anesthesia platform

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 1 Page number: 156-166 Column: 学术论文—智能系统 Public date: 2026-03-05
Title:
MorpheusAPI: an LLM Agent for intelligent anesthesia platform
Author(s):
WANG Jing1; 2;  SHEN Le3;  LIN Fei1;  ZHANG Mengmeng2; 4;  HUANG Jun1;  NI Qinghua1;  TIAN Yonglin2;  LAN Ling3;  YE Peijun2;  LYU Yisheng2;  WANG Feiyue
1. Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China;
2. the State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
3. Department of Anesthesiology, Peking Union Medical College Hospital, Beijing 100730, China;
4. the School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China;
5. the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Keywords:
anesthetics; multi-agent systems; large language models; artificial intelligence; intelligent agents; decision support systems; medical computing; risk assessment
CLC:
TP391
DOI:
10.11992/tis.202505004
Abstract:
To address the reliance of traditional perioperative anesthesia management models on clinical guidelines and the clinical judgment of anesthesiologists, which causes anesthesiologists to bear a huge workload and decision-making pressure when faced with massive real-time physiological data, complex individualized patient conditions, and rapidly changing high-risk scenarios, MorpheusAPI, a multi-agent, intelligent anesthesia platform based on large language models(LLMs), was proposed. The platform includes an execution model and a shadow model. The execution model integrates five agents for perception, prediction, decision-making, verification and central coordination. It enables efficient integration of multi-modal data through the model context protocol, the chain-of-thought prompts to enhance risk reasoning, and retrieval-augmented generation(RAG) to ensure the reliability of clinical decisions. The anesthesia shadow model forms a closed loop that continuously optimizes the performance of the execution model. Case studies demonstrated that the risk prediction response time for the MorpheusAPI system is 0.4 s, the core reasoning delay is 10~15 ms, the propofol induction dose is successfully optimized to 2.0 mg/(kg·h), and the mean arterial pressure is maintained not less than 65 mmHg. The results verify the great potential of the model to improve anesthesia safety and efficiency and provide insights into the design and application of intelligent anesthesia systems.
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