Automated Chest CT Protocol Selection via Large Language Model Derived Text Embeddings from Imaging Request Text

תקציר מקורי באנגליתarXiv:2609.07986v1 Announce Type: new Abstract: Purpose: Accurate CT protocol selection is critical for diagnostic quality and patient safety, yet the current process is manual, time-consuming, and prone to inconsistencies. Prior Machine Learning methods using keywords or bag-of-words lack contextual understanding and perform poorly on rare protocols. We propose a decision support system using large language model (LLM) features to recommend protocols from free-text clinical indications, capturing clinical nuance and phrasing variation for more consistent, efficient selection. Methods: In this REB-approved retrospective study, 285,123 chest CT imaging requests from a large academic medical center (2017-2024) were split into training (228,099, 80%) and held-out test (57,024, 20%) sets. Each