Optimizing Large Language Models for Automated Protocoling of Abdominal and Pelvic CT Scans: The Power of Context

2026/01/01 by Bryan Buckley, Adriano B. Dias, Yangqing Deng +4 · 1 voice
Medicine · Computer Science · #Artificial Intelligence in Healthcare and Education #Machine Learning in Healthcare #Topic Modeling

paper · doi:10.1148/radiol.252105

openalex publication_date 2026/01/01 · openalex created_date 2026/01/08 · openalex updated_date 2026/07/22

Abstract

= .30). Conclusion For protocoling abdominal and pelvic CT scans, the LLM, GPT-4o, selected optimal protocols more frequently than radiologists when optimized with detailed prompting, and fine-tuning of the model did not further improve performance. © RSNA, 2026

Citations

• Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
• Machine Learning and Deep Learning Models for Automated Protocoling of Emergency Brain MRI Using Text from Clinical Referrals

Discussions

• GPT-4o selected optimal CT protocols more frequently than Radiologists [hn, 1 points, 1 comments]

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