ScaFE proposes using large language models as feature engineers to transform medical images into clinically interpretable representations. By generating deterministic Python code from established scar assessment criteria, it extracts features aligned with clinical scoring systems like the Vancouver Scar Scale. The method achieves superior performance under limited data, with advantages in data efficiency, privacy preservation, and interpretability.