SOURCE-LINKED INTELLIGENCE
Automated Chest CT Protocol Selection via Large Language Model Derived Text Embeddings from Imaging Request Text
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
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-07T21:05:36.000Z
First collected: 2026-09-20T20:32:20.942Z. This is not the publication date.