搜索医学 AI 论文与资源

ICLR 2026 Poster accepted paper at ICLR 2026. In clinical practice, physicians refrain from making decisions when patient information is insufficient. This behavior, known as abstention, is a critical safety mechanism preventing potentially harmful misdiagnoses. Recent investigations have reported the application of large language models (LLMs) in medical scenarios. However, existing LLMs struggle with the abstentions, frequently providing overconfident responses despite incomplete information. This limitation stems from conventional abstention methods relying solely on model self-assessments, which lack systematic strategies to identify knowledge boundaries with external medical evidences.

评估医疗 LLM 多智能体系统在攻击场景下的安全风险，比较多种多智能体拓扑，并提出检测与纠正机制以缓解恶意智能体造成的医疗安全问题。