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Large Language Models (LLMs) have introduced a new era of proficiency incomprehending complex healthcare and biomedical topics. However, there is anoticeable lack of models in languages other than English and models that caninterpret multi-modal input, which is crucial for global healthcareaccessibility. In response, this study introduces Qilin-Med-VL, the firstChinese large vision-language model designed to integrate the analysis oftextual and visual data. Qilin-Med-VL combines a pre-trained Vision Transformer(ViT) with a foundational LLM. It undergoes a thorough two-stage curriculumtraining process that includes feature alignment and instruction tuning. Thismethod enhances the model's ability to generate medical captions and answercomplex medical queries. We also release ChiMed-VL, a dataset consisting ofmore than 1M image-text pairs. This dataset has been carefully curated toenable detailed and comprehensive interpretation of medical data using varioustypes of images.

Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare