  Medical question answer (QA) assistants respond to lay users' health-related
queries by synthesizing information from multiple sources using natural
language processing and related techniques. They can serve as vital tools to
alleviate issues of misinformation, information overload, and complexity of
medical language, thus addressing lay users' information needs while reducing
the burden on healthcare professionals. QA systems, the engines of such
assistants, have typically used either language models (LMs) or knowledge
graphs (KG), though the approaches could be complementary. LM-based QA systems
excel at understanding complex questions and providing well-formed answers, but
are prone to factual mistakes. KG-based QA systems, which represent facts well,
are mostly limited to answering short-answer questions with pre-created
templates. While a few studies have jointly used LM and KG approaches for
text-based QA, this was done to answer multiple-choice questions. Extant QA
systems also have limitations in terms of automation and performance. We
address these challenges by designing a novel, automated disease QA system
which effectively utilizes both LM and KG techniques through a joint-reasoning
approach to answer disease-related questions appropriate for lay users. Our
evaluation of the system using a range of quality metrics demonstrates its
efficacy over benchmark systems, including the popular ChatGPT.
