  The recent success of Large Language Models (LLMs) has had a significant
impact on the healthcare field, providing patients with medical advice,
diagnostic information, and more. However, due to a lack of professional
medical knowledge, patients are easily misled by generated erroneous
information from LLMs, which may result in serious medical problems. To address
this issue, we focus on tuning the LLMs to be medical assistants who
collaborate with more experienced doctors. We first conduct a two-stage survey
by inspiration-feedback to gain a broad understanding of the real needs of
doctors for medical assistants. Based on this, we construct a Chinese medical
dataset called DoctorFLAN to support the entire workflow of doctors, which
includes 92K Q\&A samples from 22 tasks and 27 specialists. Moreover, we
evaluate LLMs in doctor-oriented scenarios by constructing the
DoctorFLAN-\textit{test} containing 550 single-turn Q\&A and DotaBench
containing 74 multi-turn conversations. The evaluation results indicate that
being a medical assistant still poses challenges for existing open-source
models, but DoctorFLAN can help them significantly. It demonstrates that the
doctor-oriented dataset and benchmarks we construct can complement existing
patient-oriented work and better promote medical LLMs research.
