DoctorAgent-RL, a reinforcement learning-based multi-agent framework, enhances multi-turn reasoning and diagnostic performance in medical consultations compared to existing systems.
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
