  The medical conversational question answering (CQA) system aims at providing
a series of professional medical services to improve the efficiency of medical
care. Despite the success of large language models (LLMs) in complex reasoning
tasks in various fields, such as mathematics, logic, and commonsense QA, they
still need to improve with the increased complexity and specialization of the
medical field. This is because medical CQA tasks require not only strong
medical reasoning, but also the ability to think broadly and deeply. In this
paper, to address these challenges in medical CQA tasks that need to be
considered and understood in many aspects, we propose the Holistically Thought
(HoT) method, which is designed to guide the LLMs to perform the diffused and
focused thinking for generating high-quality medical responses. The proposed
HoT method has been evaluated through automated and manual assessments in three
different medical CQA datasets containing the English and Chinese languages.
The extensive experimental results show that our method can produce more
correctness, professional, and considerate answers than several
state-of-the-art (SOTA) methods, manifesting its effectiveness. Our code in
https://github.com/WENGSYX/HoT.
