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From Passive to Proactive: A Hierarchical Multi-Agent Framework for Automated Medical Pre-Consultation
arXiv – CS AI|ChengZhang Yu, YingRu He, Hongyan Cheng, nuo Cheng, Zhixing Liu, Dongxu Mu, Zhangrui Shen Yang Gao, and Zhanpeng Jin||4 views
🤖AI Summary
Researchers developed a multi-agent AI system for medical triage that uses three specialized agents to improve patient classification accuracy. The system achieved 89.6% accuracy in primary department classification and 74.3% in secondary classification, addressing healthcare staffing shortages through automated pre-consultation.
Key Takeaways
- →Multi-agent AI system addresses healthcare triage challenges with three specialized agents working collaboratively.
- →System achieved 89.6% accuracy for primary department classification and 74.3% for secondary classification.
- →Framework adapts to diverse hospital configurations while maintaining high triage accuracy.
- →Research based on 3,360 real-world cases from Chinese medical network spanning 71 departments.
- →Solution targets post-pandemic healthcare demand surge and critical nursing shortages.
Read Original →via arXiv – CS AI
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