Guiding Clinical Reasoning with Large Language Models via Knowledge
  Seeds
Jiageng WU Xian Wu Jie Yang
Clinical reasoning refers to the cognitive process that physicians employ in
evaluating and managing patients. This process typically involves suggesting
necessary examinations, diagnosing patients' diseases, and deciding on
appropriate therapies, etc. Accurate clinical reasoning requires extensive
medical knowledge and rich clinical experience, setting a high bar for
physicians. This is particularly challenging in developing countries due to the
overwhelming number of patients and limited physician resources, contributing
significantly to global health inequity and necessitating automated clinical
reasoning approaches. Recently, the emergence of large language models (LLMs)
such as ChatGPT and GPT-4 have demonstrated their potential in clinical
reasoning. However, these LLMs are prone to hallucination problems, and the
reasoning process of LLMs may not align with the clinical decision path of
physicians. In this study, we introduce a novel framework, In-Context Padding
(ICP), designed to enhance LLMs with medical knowledge. Specifically, we infer
critical clinical reasoning elements (referred to as knowledge seeds) and use
these as anchors to guide the generation process of LLMs. Experiments on two
clinical question datasets demonstrate that ICP significantly improves the
clinical reasoning ability of LLMs.