迈向对话式诊断人工智能
Towards Conversational Diagnostic AI
AI总结：
本文提出基于大语言模型的诊断对话系统 AMIE，利用自我博弈模拟环境和自动化反馈机制进行训练，并在与初级保健医生的随机双盲交叉研究中展现出更高的诊断准确性和多数评估维度上的优势，标志着对话式诊断 AI 的重要进展。
摘要：
医学的核心在于医患对话，娴熟的病史采集为准确诊断、有效管理和持久信任铺平道路。能够进行诊断对话的人工智能（AI）系统可以提高医疗服务的可及性、一致性和质量。然而，逼近临床医生的专业水平仍是一项突出的重大挑战。在此，我们介绍 AMIE（Articulate Medical Intelligence Explorer，清晰医学智能探索者），一个基于大语言模型（LLM）并针对诊断对话进行优化的 AI 系统。AMIE 使用一种新颖的基于自我博弈的模拟环境，并配备自动化反馈机制，以在多种疾病状况、专科和情境中扩展学习规模。我们设计了一个评估框架，用于衡量具有临床意义的性能维度，包括病史采集、诊断准确性、管理推理、沟通技巧和同理心。我们在一项随机、双盲、交叉研究中，将 AMIE 的表现与初级保健医生（PCP）进行了比较，该研究采用客观结构化临床考试（OSCE）形式，由经过验证的患者演员进行基于文本的咨询。该研究包括来自加拿大、英国和印度临床提供者的 149 个病例场景，20 名初级保健医生用于与 AMIE 比较，并由专科医生和患者演员进行评估。根据专科医生的评价，AMIE 在 32 个维度中的 28 个维度上表现出更高的诊断准确性和更优的性能；根据患者演员的评价，AMIE 在 26 个维度中的 24 个维度上表现更优。我们的研究存在若干局限性，应谨慎解读。临床医生仅限于不熟悉的同步文本聊天，这种方式允许大规模 LLM 与患者互动，但不能代表常规临床实践。虽然在 AMIE 能够转化到真实世界环境之前还需要进一步研究，但这些结果代表了迈向对话式诊断 AI 的一个里程碑。
英文摘要：
At the heart of medicine lies the physician-patient dialogue, where skillful history-taking paves the way for accurate diagnosis, effective management, and enduring trust. Artificial Intelligence (AI) systems capable of diagnostic dialogue could increase accessibility, consistency, and quality of care. However, approximating clinicians' expertise is an outstanding grand challenge. Here, we introduce AMIE (Articulate Medical Intelligence Explorer), a Large Language Model (LLM) based AI system optimized for diagnostic dialogue. AMIE uses a novel self-play based simulated environment with automated feedback mechanisms for scaling learning across diverse disease conditions, specialties, and contexts. We designed a framework for evaluating clinically-meaningful axes of performance including history-taking, diagnostic accuracy, management reasoning, communication skills, and empathy. We compared AMIE's performance to that of primary care physicians (PCPs) in a randomized, double-blind crossover study of text-based consultations with validated patient actors in the style of an Objective Structured Clinical Examination (OSCE). The study included 149 case scenarios from clinical providers in Canada, the UK, and India, 20 PCPs for comparison with AMIE, and evaluations by specialist physicians and patient actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 28 of 32 axes according to specialist physicians and 24 of 26 axes according to patient actors. Our research has several limitations and should be interpreted with appropriate caution. Clinicians were limited to unfamiliar synchronous text-chat which permits large-scale LLM-patient interactions but is not representative of usual clinical practice. While further research is required before AMIE could be translated to real-world settings, the results represent a milestone towards conversational diagnostic AI.
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。
