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论文ICLR 2026 Poster2026 年medical LLM agent

Doctor-R1:通过体验式 Agent 强化学习掌握临床问诊

ICLR 2026 Poster accepted paper at ICLR 2026. The professionalism of a human doctor in outpatient service depends on two core abilities: the ability to make accurate medical decisions and the medical consultation skill to conduct strategic, empathetic patient inquiry. Existing Large Language Models (LLMs) have achieved remarkable accuracy on medical decision-making benchmarks. However, they often lack the ability to conduct the strategic and empathetic consultation, which is essential for real-world clinical scenarios. To address this gap, we propose Doctor-R1, an AI doctor agent trained to master both of the capabilities by ask high-yield questions and conduct strategic multi-turn inquiry to guide decision-making.

论文默认配图 - 医学影像计算

论文详情

英文标题
Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning
作者
Yunghwei Lai, Kaiming Liu, Ziyue Wang, Weizhi Ma, Yang Liu
期刊/会议
ICLR 2026 Poster
发表年份
2026 年
研究方向
medical LLM agent