A three-agent LLM system with hand-built department rules reaches 89.6 percent primary and 74.3 percent secondary department accuracy on a Chinese triage dataset after four interactive rounds.
Generalization in medical AI: a perspective on developing scalable models
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abstract
The scientific community is increasingly recognizing the importance of generalization in medical AI for translating research into practical clinical applications. A three-level scale is introduced to characterize out-of-distribution generalization performance of medical AI models. This scale addresses the diversity of real-world medical scenarios as well as whether target domain data and labels are available for model recalibration. It serves as a tool to help researchers characterize their development settings and determine the best approach to tackling the challenge of out-of-distribution generalization.
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Collaborative Medical Triage under Uncertainty: A Multi-Agent Dynamic Matching Approach
A three-agent LLM system with hand-built department rules reaches 89.6 percent primary and 74.3 percent secondary department accuracy on a Chinese triage dataset after four interactive rounds.