Efficacy of Fine-Tuned Large Language Model in CT Protocol Assignment as Clinical Decision-Supporting System.

ABSTRACT: Accurate CT protocol assignment is crucial for optimizing medical imaging procedures. The integration of large language models (LLMs) may be helpful, but its efficacy as a clinical decision support system for protocoling tasks remains unknown. This study aimed to develop and evaluate fine-tuned LLM specifically designed for CT protocoling, as well as assess its performance, both standalone and in concurrent use, in terms of effectiveness and efficiency within radiological workflows. This retrospective study included radiology tests for contrast-enhanced chest and abdominal CT examinations (2829/498/941 for training/validation/testing). Inputs involve the clinical indication section, age, and anatomic coverage. The LLM was fine-tuned for 15 epochs, selecting the best model by macro sensitiv

SUBMITTER: Kanemaru N 

PROVIDER: S-EPMC12701208 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature