RDguru: An Intelligent Agent for Rare Diseases.

Journal: AMIA ... Annual Symposium proceedings. AMIA Symposium
Published Date:

Abstract

Large language models (LLMs) have shown great promise in clinical medicine, but their adoption in real-world settings has been limited by their tendency to generate incorrect and sometimes even toxic statements. This study presents a reliable rare disease intelligent agent called RDguru, which incorporates authoritative and reliable knowledge sources and tools into the reasoning and response of LLMs. In addition to answering questions about rare diseases more accurately, RDguru can conduct medical consultations to provide differential diagnosis decision support for clinical users. The DQN-based multi-source fusion diagnostic model integrates three diagnostic recommendation strategies, GPT-4, PheLR, and phenotype matching. Testing on 238 real rare disease cases showed that RDguru's top 10 list of recommended diagnoses was able to recall 69.1% of real diagnoses, the top 5 recommended diagnoses were able to recall 63.6% of real diagnoses, and the top ranked diagnosis was able to achieve an accuracy rate of 41.9%.

Authors

  • Jian Yang
    Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK, Canada.
  • Liqi Shu
    Department of Neurology, The Warren Alpert Medical School of Brown University, Providence, RI, United States.
  • Huilong Duan
    The College of Biomedical Engineering and Instrument Science, Zhejiang University, 310027 Hangzhou, Zhejiang, China.
  • Haomin Li
    Clinical Data Center, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, 310052 Hangzhou, Zhejiang, China.