AI chatbots talk to patients in multi-turn conversations in order to imitate real doctors and meet the patients’ needs during prior medical inquiries. However, existing works ignore the fact that doctors can convey two different tones answers to patients through feedback information, that is, question and statement. This inflexible method urges us to seek a new response strategy that outputs a tone indicator and uses it to limit the range of words output. In this paper, we propose a novel chat model based on question-and-answer juxtaposition (QAJCM), which simultaneously optimize three parts: the basic response, the generated counterpart, and the type of the basic response. These are all produced from the basic response, the real result. In specific, the model outputs a question, a statement, and a type to ensure that the type gives correct choice in the first two parts. Experiments on MedDG and HaoDF datasets show that the BLEU-average scores are up to 20.28 and 3.03, which are 45% and 49% higher than the baselines, respectively. According to the experimental results, we have made obvious improvements and verify that our work can respond to patients in a correct tone in the medical inquiry scenario.