Real-World Doctor Agent with Proactive Consultation through Multi-Agent Reinforcement Learning

TL;DR

This paper introduces DoctorAgent-RL, a reinforcement learning multi-agent system trained on a new medical dialogue dataset, which improves diagnostic accuracy and interaction quality in clinical consultations.

Contribution

It presents a novel RL-based framework for medical dialogue, emphasizing strategic questioning and dynamic decision-making, with a new dataset for training and evaluation.

Findings

DoctorAgent-RL achieved a 70% exact diagnostic match rate.

The system outperformed frontier models in clinical diagnosis tasks.

Rigorous evaluations confirmed its effectiveness in real-world scenarios.

Abstract

Large language models (LLMs) struggle in real-world clinical consultations. Single-turn consultation systems require patients to describe all symptoms at once, which often leads to unclear complaints and vague diagnoses. Traditional dialogue models, constrained by static supervised learning, are limited to superficially imitating existing dialogue patterns and lack the ability to actively construct understanding in dynamic interactions, thus failing to achieve genuine clinical reasoning.To address these challenges, we propose DoctorAgent-RL, a reinforcement learning (RL)-based multi-agent collaborative framework, and train a doctor agent on Qwen2.5-7B-Instruct using this framework. Within this framework, a medical consultation is modeled as a dynamic decision-making process under uncertainty. The core intelligence of the doctor agent is shifted from knowing the answer to learning and mastering a questioning methodology aimed at achieving an optimal diagnosis. Through strategic questioning, it guides the progressive emergence of key patient information in multi-turn dialogues. To support this high-fidelity simulation of the real diagnostic process, we constructed MTMedDialog, a novel English multi-turn medical consultation dataset designed for dynamic, interactive training.To validate its real-world effectiveness, rigorous evaluations including blinded human assessments and trials with real patients were conducted. DoctorAgent-RL outperformed frontier models and achieved a 70% exact diagnostic match rate, confirming its potential as a collaborative tool. By handling initial screenings, it can free clinicians to focus on complex cases, thereby addressing critical issues like physician shortages and misdiagnosis risks while alleviating the strain on healthcare resources.