Leveraging large language and vision models for knowledge extraction from large-scale image-text colonoscopy records.

The development of artificial intelligence systems for colonoscopy analysis often necessitates expert-annotated image datasets. However, limitations in dataset size and diversity impede model performance and generalization. Image-text colonoscopy records from routine clinical practice, comprising millions of images and text reports, serve as a valuable data source, although annotating them is labour intensive. Here we leverage recent advancements in large language and vision models and propose EndoKED, a data mining paradigm for deep knowledge extraction and distillation. EndoKED automates the transformation of raw colonoscopy records into image datasets with pixel-level annotation. We apply EndoKED to multicentre datasets of raw colonoscopy records (~1 million images), showing its superior performance in detecting polyps at the report and image levels, as well as annotating polyps at the pixel level. The state-of-the-art performance and generalization ability of polyp segmentation models are achieved through EndoKED pretraining. Furthermore, the EndoKED vision backbone enables data-efficient learning for optical biopsy, achieving expert-level performance in internal, external and prospective validation datasets.