前瞻性临床可行性研究：一种对话式诊断AI在门诊初级保健诊所中的应用
A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic
AI总结：
本文探讨了基于大语言模型的对话式AI在真实临床环境中的可行性，评估了其安全性、质量和临床推理能力，并展示了AI在初级保健中的初步应用效果。
摘要：
基于大语言模型（LLM）的AI系统在模拟环境中展示了在患者对话中的诊断和管理潜力。将其应用于临床实践需要在真实工作流程中进行评估，并有严格的安全监督。我们报告了一项前瞻性、单臂可行性研究，评估了基于LLM的对话式AI（Articulate Medical Intelligence Explorer，AMIE）在门诊初级保健诊所中的应用，用于患者与提供者讨论的临床病史采集和潜在诊断的呈现。100名成人患者在就诊前最多5天完成了与AMIE的文本聊天互动。我们旨在评估对话的安全性和质量、患者和医生的经验，以及与初级保健提供者（PCPs）的临床推理能力的比较。人类安全监督员实时监控所有患者与AMIE的互动，并未需要干预以停止任何咨询，基于预定义的标准。患者报告了高满意度，与AMIE互动后，其对AI的态度有所改善（p < 0.001）。医生发现AMIE的输出有用，对准备有积极影响。AMIE的鉴别诊断（DDx）在8周后根据病历审查，在90%的病例中包括最终诊断，其中75%的准确率在前三名中。对AMIE和医生DDx和管理（Mx）计划的盲评显示，总体DDx和Mx计划质量相似，无显著差异（p = 0.6）和Mx的适当性和安全性（p = 0.1和1.0）。医生在Mx的实用性（p = 0.003）和成本效益（p = 0.004）方面优于AMIE。尽管需要进一步研究，但本研究展示了对话式AI在真实环境中的初步可行性、安全性和用户接受度，代表了向临床应用迈出的关键一步。
英文摘要：
Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Translating these systems into clinical practice requires assessment in real-world workflows with rigorous safety oversight. We report a prospective, single-arm feasibility study of an LLM-based conversational AI, the Articulate Medical Intelligence Explorer (AMIE), conducting clinical history taking and presentation of potential diagnoses for patients to discuss with their provider at urgent care appointments at a leading academic medical center. 100 adult patients completed an AMIE text-chat interaction up to 5 days before their appointment. We sought to assess the conversational safety and quality, patient and clinician experience, and clinical reasoning capabilities compared to primary care providers (PCPs). Human safety supervisors monitored all patient-AMIE interactions in real time and did not need to intervene to stop any consultations based on pre-defined criteria. Patients reported high satisfaction and their attitudes towards AI improved after interacting with AMIE (p < 0.001). PCPs found AMIE's output useful with a positive impact on preparedness. AMIE's differential diagnosis (DDx) included the final diagnosis, per chart review 8 weeks post-encounter, in 90% of cases, with 75% top-3 accuracy. Blinded assessment of AMIE and PCP DDx and management (Mx) plans suggested similar overall DDx and Mx plan quality, without significant differences for DDx (p = 0.6) and appropriateness and safety of Mx (p = 0.1 and 1.0, respectively). PCPs outperformed AMIE in the practicality (p = 0.003) and cost effectiveness (p = 0.004) of Mx. While further research is needed, this study demonstrates the initial feasibility, safety, and user acceptance of conversational AI in a real-world setting, representing crucial steps towards clinical translation.
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