One-shot synthesis of rare gastrointestinal lesions improves diagnostic accuracy and clinical training

Rare gastrointestinal lesions are infrequently encountered in routine endoscopy, limiting data for developing reliable artificial intelligence (AI) models and training novice clinicians. Here we present EndoRare, a one-shot, retraining-free generative framework that synthesizes diverse lesion exemplars from a single reference image. Using language-guided concept disentanglement, EndoRare separates pathognomonic lesion features from non-diagnostic attributes, encoding the former into a learnable prototype embedding while varying the latter to ensure diversity. We validate EndoRare across four rare pathologies: calcifying fibrous tumor, juvenile polyposis syndrome, familial adenomatous polyposis, and Peutz-Jeghers syndrome. Experts judge the synthetic images clinically plausible, and data augmentation significantly improves downstream AI classifiers, increasing true positive rates at low false-positive rates. In an exploratory reader study, exposure to EndoRare-generated cases is associated with improved image-level recognition by novice endoscopists. These findings support the potential use of EndoRare as a data-efficient tool for rare-lesion education and motivate prospective controlled validation.