耐心询问患者：通过 grounded 推理实现面向人类的医疗对话 LLM
Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning
AI总结：
本文提出 APP，一种基于 LLM 的医疗助手，通过 grounded 推理和透明诊断提升医疗对话的人性化体验，改进诊断准确性并增强用户参与度。
摘要：
医疗医生短缺限制了及时可靠的医疗访问，留下数百万患者未得到充分照顾。大型语言模型（LLMs）提供了潜在解决方案，但在真实临床互动中面临挑战。许多 LLMs 未基于权威医学指南，无法透明管理诊断不确定性。其语言往往僵硬机械，缺乏对患者信任至关重要的人性化特质。为此，我们提出 Ask Patients with Patience（APP），一种多轮次 LLM 基础医疗助手，旨在实现 grounded 推理、透明诊断和人性化交互。APP 通过富有同理心的对话获取用户症状，显著提高可及性和用户参与度。它还整合了贝叶斯主动学习以支持透明和适应性诊断。该框架基于已验证的医学指南，确保临床基础和证据导向的推理。为评估其性能，我们开发了一个新基准，使用从真实世界咨询案例中提取的患者代理模拟现实医疗对话。我们比较了 APP 与 SOTA 一次性及多轮次 LLM 基线。结果表明，APP 提高了诊断准确性，减少了不确定性，并增强了用户体验。通过将医学专业知识与透明、人性化交互相结合，APP 桥接了 AI 驱动医疗援助与现实临床实践之间的差距。
英文摘要：
The severe shortage of medical doctors limits access to timely and reliable healthcare, leaving millions underserved. Large language models (LLMs) offer a potential solution but struggle in real-world clinical interactions. Many LLMs are not grounded in authoritative medical guidelines and fail to transparently manage diagnostic uncertainty. Their language is often rigid and mechanical, lacking the human-like qualities essential for patient trust. To address these challenges, we propose Ask Patients with Patience (APP), a multi-turn LLM-based medical assistant designed for grounded reasoning, transparent diagnoses, and human-centric interaction. APP enhances communication by eliciting user symptoms through empathetic dialogue, significantly improving accessibility and user engagement. It also incorporates Bayesian active learning to support transparent and adaptive diagnoses. The framework is built on verified medical guidelines, ensuring clinically grounded and evidence-based reasoning. To evaluate its performance, we develop a new benchmark that simulates realistic medical conversations using patient agents driven by profiles extracted from real-world consultation cases. We compare APP against SOTA one-shot and multi-turn LLM baselines. The results show that APP improves diagnostic accuracy, reduces uncertainty, and enhances user experience. By integrating medical expertise with transparent, human-like interaction, APP bridges the gap between AI-driven medical assistance and real-world clinical practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
