论文概要
研究领域: NLP 作者: John Ray Martinez 发布时间: 2026-03-25 arXiv: 2603.24481
中文摘要
校准不当的置信度分数是将AI部署到临床环境中的实际障碍。一个总是过度自信的模型无法为延迟决策提供有用的信号。本文提出了一种多智能体框架，结合领域特定的专家智能体、两阶段验证和S分数加权融合，以改进医学多项选择题回答中的校准和区分能力。四个专家智能体（呼吸科、心脏科、神经科、胃肠科）使用Qwen2.5-7B-Instruct生成独立诊断。
原文摘要
Miscalibrated confidence scores are a practical obstacle to deploying AI in clinical settings. A model that is always overconfident offers no useful signal for deferral. We present a multi-agent framework that combines domain-specific specialist agents with Two-Phase Verification and S-Score Weighted Fusion to improve both calibration and discrimination in medical multiple-choice question answering. Four specialist agents (respiratory, cardiology, neurology, gastroenterology) generate independent diagnoses using Qwen2.5-7B-Instruct.
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