  Medical dialogue systems have attracted significant attention for their
potential to act as medical assistants. Enabling these medical systems to
emulate clinicians' diagnostic reasoning process has been the long-standing
research focus. Previous studies rudimentarily realized the simulation of
clinicians' diagnostic process by fine-tuning language models on high-quality
dialogue datasets. Nonetheless, they overly focus on the outcomes of the
clinician's reasoning process while ignoring their internal thought processes
and alignment with clinician preferences. Our work aims to build a medical
dialogue system that aligns with clinicians' diagnostic reasoning processes. We
propose a novel framework, Emulation, designed to generate an appropriate
response that relies on abductive and deductive diagnostic reasoning analyses
and aligns with clinician preferences through thought process modeling.
Experimental results on two datasets confirm the efficacy of Emulation.
Crucially, our framework furnishes clear explanations for the generated
responses, enhancing its transparency in medical consultations.
