We read with great interest the article by Lindholz et al., “Comparing large language models and text embedding models for automated classification of textual, semantic, and critical changes in radiology reports” [1]. The authors should be commended for addressing an important and often overlooked aspect of radiological practice: the systematic evaluation of changes between preliminary reports authored by residents and finalized versions approved by attending radiologists. Their study is highly …