  Multimodal Large Language Models (MLLMs) have shown success in various
general image processing tasks, yet their application in medical imaging is
nascent, lacking tailored models. This study investigates the potential of
MLLMs in improving the understanding and generation of Chest X-Rays (CXRs). We
introduce MedXChat, a unified framework facilitating seamless interactions
between medical assistants and users for diverse CXR tasks, including text
report generation, visual question-answering (VQA), and Text-to-CXR generation.
Our MLLMs using natural language as the input breaks task boundaries, maximally
simplifying medical professional training by allowing diverse tasks within a
single environment. For CXR understanding, we leverage powerful off-the-shelf
visual encoders (e.g., ViT) and LLMs (e.g., mPLUG-Owl) to convert medical
imagery into language-like features, and subsequently fine-tune our large
pre-trained models for medical applications using a visual adapter network and
a delta-tuning approach. For CXR generation, we introduce an innovative
synthesis approach that utilizes instruction-following capabilities within the
Stable Diffusion (SD) architecture. This technique integrates smoothly with the
existing model framework, requiring no extra parameters, thereby maintaining
the SD's generative strength while also bestowing upon it the capacity to
render fine-grained medical images with high fidelity. Through comprehensive
experiments, our model demonstrates exceptional cross-task adaptability,
displaying adeptness across all three defined tasks. Our MedXChat model and the
instruction dataset utilized in this research will be made publicly available
to encourage further exploration in the field.
