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.

翻译：临床推理是指医生在评估和管理患者时所采用的认知过程。该过程通常涉及建议必要的检查、诊断患者疾病以及决定适当治疗方案等。准确的临床推理需要广泛的医学知识和丰富的临床经验，对医生提出了很高的要求。这在发展中国家尤为困难，由于患者数量庞大而医生资源有限，这显著加剧了全球健康不平等问题，因此迫切需要自动化临床推理方法。近年来，ChatGPT和GPT-4等大语言模型的出现展示了其在临床推理方面的潜力。然而，这些大语言模型容易出现幻觉问题，且其推理过程可能与医生的临床决策路径不一致。在本研究中，我们引入了一种新颖的框架——上下文填充（In-Context Padding, ICP），旨在用医学知识增强大语言模型。具体来说，我们推断关键的临床推理要素（称为知识种子），并将其作为锚点来引导大语言模型的生成过程。在两个临床问题数据集上的实验表明，ICP显著提升了大语言模型的临床推理能力。