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<p>While Large Language Models (LLMs) have demonstrated significant potential in medical reasoning, existing research is often conducted under the idealized assumption of complete information, which contrasts with the reality of incomplete information in clinical practice. To address this gap, this paper proposes a novel interactive reasoning framework designed to empower LLMs with the ability to proactively ask questions to gather critical information. Our core methodology involves a Knowledge Graph (KG) Reasoner that explores a professional medical KG to identify the most crucial information gaps, thereby providing strategic guidance for the LLM’s questioning. Furthermore, we introduce a confidence estimation mechanism inspired by the clinical process of differential diagnosis, enabling the system to accurately assess its own uncertainty and trigger questions when necessary, rather than making premature decisions. To validate our approach, we conducted experiments on an interactive medical question-answering benchmark. The results demonstrate that, compared to existing baselines like MedIQ, our framework can effectively gather key patient information through more strategic questioning and avoid errors caused by premature diagnostic closure. Our model achieves a significant improvement in final question-answering accuracy, proving that the proposed KG-guided questioning strategy is a viable path toward making AI models more aligned with real-world clinical workflows, thereby enhancing their reliability and safety.</p>

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KG-guided proactive questioning for LLMs in multi-turn interactive medical reasoning

  • 期刊论文

  • 发表日期：

    2026年4月6日

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  • Yu-Chen Li,
  • Tzu-Ni Yang,
  • Li-Chen Fu,
  • Yung-Jen Hsu

摘要

While Large Language Models (LLMs) have demonstrated significant potential in medical reasoning, existing research is often conducted under the idealized assumption of complete information, which contrasts with the reality of incomplete information in clinical practice. To address this gap, this paper proposes a novel interactive reasoning framework designed to empower LLMs with the ability to proactively ask questions to gather critical information. Our core methodology involves a Knowledge Graph (KG) Reasoner that explores a professional medical KG to identify the most crucial information gaps, thereby providing strategic guidance for the LLM’s questioning. Furthermore, we introduce a confidence estimation mechanism inspired by the clinical process of differential diagnosis, enabling the system to accurately assess its own uncertainty and trigger questions when necessary, rather than making premature decisions. To validate our approach, we conducted experiments on an interactive medical question-answering benchmark. The results demonstrate that, compared to existing baselines like MedIQ, our framework can effectively gather key patient information through more strategic questioning and avoid errors caused by premature diagnostic closure. Our model achieves a significant improvement in final question-answering accuracy, proving that the proposed KG-guided questioning strategy is a viable path toward making AI models more aligned with real-world clinical workflows, thereby enhancing their reliability and safety.