  The latest breakthroughs in large vision-language models, such as Bard and
GPT-4, have showcased extraordinary abilities in performing a wide range of
tasks. Such models are trained on massive datasets comprising billions of
public image-text pairs with diverse tasks. However, their performance on
task-specific domains, such as radiology, is still under-investigated and
potentially limited due to a lack of sophistication in understanding biomedical
images. On the other hand, conversational medical models have exhibited
remarkable success but have mainly focused on text-based analysis. In this
paper, we introduce XrayGPT, a novel conversational medical vision-language
model that can analyze and answer open-ended questions about chest radiographs.
Specifically, we align both medical visual encoder (MedClip) with a fine-tuned
large language model (Vicuna), using a simple linear transformation. This
alignment enables our model to possess exceptional visual conversation
abilities, grounded in a deep understanding of radiographs and medical domain
knowledge. To enhance the performance of LLMs in the medical context, we
generate ~217k interactive and high-quality summaries from free-text radiology
reports. These summaries serve to enhance the performance of LLMs through the
fine-tuning process. Our approach opens up new avenues the research for
advancing the automated analysis of chest radiographs. Our open-source demos,
models, and instruction sets are available at:
https://github.com/mbzuai-oryx/XrayGPT.
