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MACD: Multi-Agent Clinical Diagnosis with Self-Learned Knowledge for LLM
arXiv – CS AI|Wenliang Li, Rui Yan, Xu Zhang, Li Chen, Hongji Zhu, Jing Zhao, Junjun Li, Mengru Li, Wei Cao, Zihang Jiang, Wei Wei, Kun Zhang, Shaohua Kevin Zhou||6 views
🤖AI Summary
Researchers developed MACD, a Multi-Agent Clinical Diagnosis framework that enables large language models to self-learn clinical knowledge and improve medical diagnosis accuracy. The system achieved up to 22.3% improvement over clinical guidelines and 16% improvement over physician-only diagnosis when tested on 4,390 real-world patient cases.
Key Takeaways
- →MACD framework allows LLMs to accumulate and apply clinical experience through a multi-agent pipeline, mimicking how physicians develop expertise.
- →Testing on 4,390 patient cases across seven diseases showed up to 22.3% improvement in diagnostic accuracy over established clinical guidelines.
- →Human-AI collaborative workflow demonstrated 18.6% improvement over physician-only diagnosis, highlighting synergistic potential.
- →The self-learned clinical knowledge exhibits cross-model stability and transferability across different LLMs.
- →Framework was successfully tested on multiple open-source models including Llama-3.1 8B/70B and DeepSeek-R1-Distill-Llama 70B.
#llm#medical-ai#healthcare#multi-agent#clinical-diagnosis#machine-learning#ai-research#human-ai-collaboration
Read Original →via arXiv – CS AI
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