Machine reasoning has made great progress in recent years owing to large
language models (LLMs). In the clinical domain, however, most NLP-driven
projects mainly focus on clinical classification or reading comprehension, and
under-explore clinical reasoning for disease diagnosis due to the expensive
rationale annotation with clinicians. In this work, we present a
``reasoning-aware'' diagnosis framework that rationalizes the diagnostic
process via prompt-based learning in a time- and labor-efficient manner, and
learns to reason over the prompt-generated rationales. Specifically, we address
the clinical reasoning for disease diagnosis, where the LLM generates
diagnostic rationales providing its insight on presented patient data and the
reasoning path towards the diagnosis, namely Clinical Chain-of-Thought
(Clinical CoT). We empirically demonstrate LLMs/LMs' ability of clinical
reasoning via extensive experiments and analyses on both rationale generation
and disease diagnosis in various settings. We further propose a novel set of
criteria for evaluating machine-generated rationales' potential for real-world
clinical settings, facilitating and benefiting future research in this area.

借助基于提示的学习，通过 “推理感知” 的诊断框架实现了对临床推理进行理性化，并能够在时间和劳动资源上具有高效性，从而实现了对疾病诊断的临床推理。