Ensuring the authenticity of generated disease names is crucial to prevent bias and misinformation in models. One way to enhance authenticity is by implementing rigorous semantic filtering techniques post data augmentation. This involves evaluating the similarity between the unnormalized and standard disease names at both character level (using normalized n-gram matching) and contextual semantic level (utilizing cosine similarity with BERT embeddings). By setting thresholds for these metrics, only pairs that meet a certain level of similarity are retained, filtering out potentially inaccurate or fictitious disease names.