  When Large Vision Language Models (LVLMs) are applied to multimodal medical
generative tasks, they suffer from significant model hallucination issues. This
severely impairs the model's generative accuracy, making it challenging for
LVLMs to be implemented in real-world medical scenarios to assist doctors in
diagnosis. Enhancing the training data for downstream medical generative tasks
is an effective way to address model hallucination. Moreover, the limited
availability of training data in the medical field and privacy concerns greatly
hinder the model's accuracy and generalization capabilities. In this paper, we
introduce a method that mimics human cognitive processes to construct
fine-grained instruction pairs and apply the concept of chain-of-thought (CoT)
from inference scenarios to training scenarios, thereby proposing a method
called MedThink. Our experiments on various LVLMs demonstrate that our novel
data construction method tailored for the medical domain significantly improves
the model's performance in medical image report generation tasks and
substantially mitigates the hallucinations. All resources of this work will be
released soon.
