“DrHouse: An LLM-empowered Diagnostic Reasoning System through Harnessing Outcomes from Sensor Data and Expert Knowledge” was recently accepted and published at ACM IMWUT (Interactive, Mobile, Wearable and Ubiquitous Technologies).
In this paper, we introduce DrHouse, a novel LLM-based multi-turn consultation virtual doctor system, to take advantage of daily data from smart devices in LLM-based diagnostic reasoning. DrHouse achieves 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 knowledge bases to ensure its 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 a 31.5% 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% test subjects are willing to use DrHouse.
This work is in collaboration with the AIoT Lab at The Chinese University of Hong Kong.