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
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.
