Our approach has several limitations. Firstly, errors in historical mappings may propagate into future predictions, potentially reinforcing inaccuracies over time. This challenge necessitates periodic human review and correction to prevent systematic errors. Secondly, cosine similarity between embeddings may not always provide a reliable confidence estimate, due to the considerable overlap between the score distributions of hits and misses. This makes it difficult to differentiate between correct and incorrect matches. Incorporating additional uncertainty modeling or ranking refinements could improve result interpretability. Thirdly, while our retrieval model significantly improves over rule-based methods, its performance is still constrained by the lack of a specialized Romanian medical language model. A dedicated medical NLP model trained on domain-specific Romanian corpora could further enhance accuracy.