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As advances in large language models (LLMs) and multimodal techniquescontinue to mature, the development of general-purpose multimodal largelanguage models (MLLMs) has surged, with significant applications in naturalimage interpretation. However, the field of pathology has largely remaineduntapped in this regard, despite the growing need for accurate, timely, andpersonalized diagnostics. To bridge the gap in pathology MLLMs, we present thePathAsst in this study, which is a generative foundation AI assistant torevolutionize diagnostic and predictive analytics in pathology. To developPathAsst, we collect over 142K high-quality pathology image-text pairs from avariety of reliable sources, including PubMed, comprehensive pathologytextbooks, reputable pathology websites, and private data annotated bypathologists. Leveraging the advanced capabilities of ChatGPT/GPT-4, wegenerate over 180K instruction-following samples. Furthermore, we deviseadditional instruction-following data, specifically tailored for the invocationof the pathology-specific models, allowing the PathAsst to effectively interactwith these models based on the input image and user intent, consequentlyenhancing the model's diagnostic capabilities. Subsequently, our PathAsst istrained based on Vicuna-13B language model in coordination with the CLIP visionencoder. The results of PathAsst show the potential of harnessing theAI-powered generative foundation model to improve pathology diagnosis andtreatment processes. We are committed to open-sourcing our meticulously curateddataset, as well as a comprehensive toolkit designed to aid researchers in theextensive collection and preprocessing of their own datasets. Resources can beobtained athttps://github.com/superjamessyx/Generative-Foundation-AI-Assistant-for-Pathology.

PathAsst: Redefining Pathology through Generative Foundation AI Assistant for Pathology