RDguru: A Conversational Intelligent Agent for Rare Diseases

作者：Jian Yang, Liqi Shu, Huilong Duan, Haomin Li · 发表于：IEEE Journal of Biomedical and Health Informatics · 年份：2024 · DOI：10.1109/jbhi.2024.3464555 · 被引用次数：23 · 研究领域：AI in Service Interactions、FinTech, Crowdfunding, Digital Finance、Artificial Intelligence in Healthcare

Large language models (LLMs) hold significant promise in clinical practice, yet their real-world adoption is constrained by their propensity to produce erroneous and occasionally harmful outputs, particularly in the intricate domain of rare diseases (RDs). This study introduces RDguru, a conversational intelligent agent leveraging the LangChain framework and powered by GPT-3.5-turbo. RDguru offers a comprehensive suite of functionalities, encompassing evidence-traceable knowledge Q&A and professional medical consultations for differential diagnosis (DDX), integrating authoritative knowledge sources and reliable tools. A novel multi-source fusion diagnostic model, rooted in deep Q-network, amalgamates three diagnostic recommendation strategies (GPT-4, PheLR, and phenotype matching) to enhance diagnostic recall during medical consultations. Through tailored tools and advanced algorithms for retrieval-augmented generation, RDguru excels in knowledge Q&A, automated phenotype annotation, and RD DDX. A multi-aspect Q&A analysis demonstrates RDguru outperforms ChatGPT in generating descriptions aligned with authoritative knowledge, quantified by ROUGE scores, GPT-4-based automatic rating, and RAGAs evaluation metrics. Testing on 238 published RD cases reveals that RDguru's top 5 multi-source fusion diagnoses recapture 63.87% of actual diagnoses, marking a 5.47% improvement over the state-of-the-art diagnostic method PheLR. Furthermore, RDguru's consultation strategy proves effective...