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New dual-agent framework generates realistic medical dialogues

Researchers have developed CoRPS-Dial, a novel dual-agent framework designed to generate realistic medical dialogues for pre-consultation scenarios. This approach separates patient personas from clinical workflow rules, allowing for greater control and diversity in synthetic data. The system involves a patient agent driven by personas and a doctor agent guided by clinical guidelines, with post-hoc checks for consistency. Experiments show CoRPS-Dial effectively improves the alignment of synthetic dialogues with real-world data in terms of information coverage and clinical compliance, demonstrating its utility for medical dialogue tasks. AI

IMPACT This framework could significantly improve the creation of synthetic medical data, aiding in the training of AI models for clinical applications without compromising patient privacy.

RANK_REASON The cluster contains a research paper detailing a new methodology for medical dialogue generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New dual-agent framework generates realistic medical dialogues

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The cluster contains a research paper detailing a new methodology for medical dialogue generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Qiyuan Li, Xiaomei Guo, Chao Gao, Haijiang Liu, Deyu Chen, Caicai Guo, Jinguang Gu ·

    Decoupling Patient Personas and Clinical Workflows: A Dual-Agent Approach to Realistic Medical Dialogue Generation

    arXiv:2610.32443v1 Announce Type: cross Abstract: Pre-consultation medical dialogues are essential for collecting comprehensive patient information, but are costly to obtain at scale due to privacy and ethical constraints. Existing LLM-based synthesis methods often rely on prompt…