LLMs can transfer medical knowledge into auditable, locally-executable feature programs instead of making direct predictions—improving data efficiency, privacy, and reproducibility in medical image classification.
ScaFE uses an LLM to generate executable programs that measure clinical scar features from photos, rather than asking the model to diagnose directly. These programs run locally, protecting patient data, and feed structured features into a simple Random Forest classifier.