Large language models (LLMs) have the potential to transform digital healthcare, as evidenced by recent advances in LLM-based virtual doctors. However, current approaches rely on patient's subjective descriptions of symptoms, causing increased misdiagnosis. Recognizing the value of daily data from smart devices, we introduce a novel LLM-based multi-turn consultation virtual doctor system, DrHouse, which incorporates three significant contributions: 1) It utilizes sensor data from smart devices in the diagnosis process, enhancing accuracy and reliability. 2) DrHouse leverages continuously updating medical databases such as Up-to-Date and PubMed to ensure our model remains at diagnostic standard's forefront. 3) DrHouse introduces a novel diagnostic algorithm that concurrently evaluates potential diseases and their likelihood, facilitating more nuanced and informed medical assessments. Through multi-turn interactions, DrHouse determines the next steps, such as accessing daily data from smart devices or requesting in-lab tests, and progressively refines its diagnoses. Evaluations on three public datasets and our self-collected datasets show that DrHouse can achieve up to an 18.8% increase in diagnosis accuracy over the state-of-the-art baselines. The results of a 32-participant user study show that 75% medical experts and 91.7% patients are willing to use DrHouse.

翻译：大型语言模型（LLMs）有潜力变革数字医疗，近期基于LLM的虚拟医生所取得的进展证明了这一点。然而，当前方法依赖患者主观描述的症状，导致误诊率增加。认识到智能设备日常数据的价值，我们提出了一种新颖的基于LLM的多轮问诊虚拟医生系统DrHouse，其包含三项重要贡献：1）在诊断过程中利用智能设备的传感器数据，提升准确性和可靠性；2）DrHouse借助持续更新的医学数据库（如Up-to-Date和PubMed），确保模型始终处于诊断标准前沿；3）DrHouse引入一种新型诊断算法，可同时评估潜在疾病及其可能性，促进更细致、更明智的医学评估。通过多轮交互，DrHouse决定下一步行动（如访问智能设备日常数据或请求实验室检测），并逐步优化诊断结果。在三个公开数据集及自采数据集上的评估表明，DrHouse的诊断准确率较最先进基线方法最高提升18.8%。一项包含32名参与者的用户研究结果显示，75%的医学专家和91.7%的患者愿意使用DrHouse。