title: DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning
    System for Multi-Turn Clinical Dialogue
summary: Large language models (LLMs) have demonstrated excellent capabilities in the
  field of biomedical question answering, but their application in real-world
  clinical consultations still faces core challenges. Existing systems rely on a
  one-way information transmission mode where patients must fully describe their
  symptoms in a single round, leading to nonspecific diagnostic recommendations
  when complaints are vague. Traditional multi-turn dialogue methods based on
  supervised learning are constrained by static data-driven paradigms, lacking
  generalizability and struggling to intelligently extract key clinical
  information. To address these limitations, we propose DoctorAgent-RL, a
  reinforcement learning (RL)-based multi-agent collaborative framework that
  models medical consultations as a dynamic decision-making process under
  uncertainty. The doctor agent continuously optimizes its questioning strategy
  within the RL framework through multi-turn interactions with the patient agent,
  dynamically adjusting its information-gathering path based on comprehensive
  rewards from the Consultation Evaluator. This RL fine-tuning mechanism enables
  LLMs to autonomously develop interaction strategies aligned with clinical
  reasoning logic, rather than superficially imitating patterns in existing
  dialogue data. Notably, we constructed MTMedDialog, the first English
  multi-turn medical consultation dataset capable of simulating patient
  interactions. Experiments demonstrate that DoctorAgent-RL outperforms existing
  models in both multi-turn reasoning capability and final diagnostic
  performance, demonstrating practical value in assisting clinical consultations.
  https://github.com/JarvisUSTC/DoctorAgent-RL
paper_authors._id: 683522abd68b329aeb799c46
paper_authors.name: Yichun Feng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:48:52.212Z
paper_authors._id: 67b6e2769fd5603fe15b84b9
paper_authors.avatarUrl: /avatars/e6b42c49954a840518bc3e3aa2617912.svg
paper_authors.fullname: fengyichun
paper_authors.type: user
paper_authors.user: fengyichun
paper_authors._id: 683522abd68b329aeb799c47
paper_authors.name: Jiawei Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:52:01.364Z
paper_authors._id: 64060b49a577649430bf6974
paper_authors.avatarUrl: /avatars/74d0d6ed656b593e4c101b09edf18c7a.svg
paper_authors.fullname: Jiawei Wang
paper_authors.type: user
paper_authors.user: Jarvis1111
paper_authors._id: 683522abd68b329aeb799c48
paper_authors.name: Lu Zhou
paper_authors._id: 683522abd68b329aeb799c49
paper_authors.name: Yixue Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:49:04.721Z
paper_authors._id: 666feab14809327d6b44b340
paper_authors.avatarUrl: /avatars/64e963dd6f40df3c26cdddf21c856830.svg
paper_authors.fullname: liyixue
paper_authors.type: user
paper_authors.user: lyxchelsea
paper_title: DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning
    System for Multi-Turn Clinical Dialogue
paper_summary: Large language models (LLMs) have demonstrated excellent capabilities in the
  field of biomedical question answering, but their application in real-world
  clinical consultations still faces core challenges. Existing systems rely on a
  one-way information transmission mode where patients must fully describe their
  symptoms in a single round, leading to nonspecific diagnostic recommendations
  when complaints are vague. Traditional multi-turn dialogue methods based on
  supervised learning are constrained by static data-driven paradigms, lacking
  generalizability and struggling to intelligently extract key clinical
  information. To address these limitations, we propose DoctorAgent-RL, a
  reinforcement learning (RL)-based multi-agent collaborative framework that
  models medical consultations as a dynamic decision-making process under
  uncertainty. The doctor agent continuously optimizes its questioning strategy
  within the RL framework through multi-turn interactions with the patient agent,
  dynamically adjusting its information-gathering path based on comprehensive
  rewards from the Consultation Evaluator. This RL fine-tuning mechanism enables
  LLMs to autonomously develop interaction strategies aligned with clinical
  reasoning logic, rather than superficially imitating patterns in existing
  dialogue data. Notably, we constructed MTMedDialog, the first English
  multi-turn medical consultation dataset capable of simulating patient
  interactions. Experiments demonstrate that DoctorAgent-RL outperforms existing
  models in both multi-turn reasoning capability and final diagnostic
  performance, demonstrating practical value in assisting clinical consultations.
  https://github.com/JarvisUSTC/DoctorAgent-RL
paper_ai_summary: DoctorAgent-RL, a reinforcement learning-based multi-agent framework, enhances multi-turn reasoning and diagnostic performance in medical consultations compared to existing systems.
paper_ai_keywords: reinforcement learning multi-agent collaborative framework dynamic decision-making uncertainty questioning strategy interaction strategy clinical reasoning multi-turn medical consultation dataset diagnostic performance