The latest breakthroughs in large language models (LLMs) and vision-language models (VLMs) have showcased promising capabilities toward performing a wide range of tasks. Such models are typically trained on massive datasets comprising billions of image-text pairs with diverse tasks. However, their performance on task-specific domains, such as radiology, is still under-explored. While few works have recently explored LLMs-based conversational medical models, they mainly focus on text-based analysis. In this paper, we introduce XrayGPT, a conversational medical vision-language (VLMs) model that can analyze and answer open-ended questions about chest radiographs. Specifically, we align both medical visual encoder with a fine-tuned LLM to possess visual conversation abilities, grounded in an understanding of radiographs and medical knowledge. For improved alignment of chest radiograph data, we generate ~217k interactive and high-quality summaries from free-text radiology reports. Extensive experiments are conducted to validate the merits of XrayGPT. To conduct an expert evaluation, certified medical doctors evaluated the output of our XrayGPT on a test subset and the results reveal that more than 70% of the responses are scientifically accurate, with an average score of 4/5. We hope our simple and effective method establishes a solid baseline, facilitating future research toward automated analysis and summarization of chest radiographs. Code, models, and instruction sets will be publicly released.

Accuracy of English-language Question Answering (QA) systems has improved significantly in recent years with the advent of Transformer-based models (e.g., BERT). These models are pre-trained in a self-supervised fashion with a large English text corpus and further fine-tuned with a massive English QA dataset (e.g., SQuAD). However, QA datasets on such a scale are not available for most of the other languages. Multi-lingual BERT-based models (mBERT) are often used to transfer knowledge from high-resource languages to low-resource languages. Since these models are pre-trained with huge text corpora containing multiple languages, they typically learn language-agnostic embeddings for tokens from different languages. However, directly training an mBERT-based QA system for low-resource languages is challenging due to the paucity of training data. In this work, we augment the QA samples of the target language using translation and transliteration into other languages and use the augmented data to fine-tune an mBERT-based QA model, which is already pre-trained in English. Experiments on the Google ChAII dataset show that fine-tuning the mBERT model with translations from the same language family boosts the question-answering performance, whereas the performance degrades in the case of cross-language families. We further show that introducing a contrastive loss between the translated question-context feature pairs during the fine-tuning process, prevents such degradation with cross-lingual family translations and leads to marginal improvement. The code for this work is available at https://github.com/gokulkarthik/mucot.