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LLM accurately selects chest CT protocols from clinical text

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a decision support system that uses a large language model (LLM) to automate the selection of chest CT protocols. This system, which leverages text embeddings from clinical imaging requests, aims to improve diagnostic quality and patient safety by addressing the manual and inconsistent nature of current protocol selection methods. The LLM-based approach demonstrated strong performance, achieving an overall accuracy of 79% across 18 CT protocols and comparable accuracy to radiologists on independent cases, suggesting its potential as a foundation for future protocol recommendation tools. AI

IMPACT This research demonstrates the potential for LLMs to improve efficiency and accuracy in medical imaging protocol selection, potentially leading to better patient outcomes.

RANK_REASON Academic paper detailing a novel application of LLMs in a medical context. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM accurately selects chest CT protocols from clinical text

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Academic paper detailing a novel application of LLMs in a medical context. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zahra Hosseini, Mahan Pouromidi, Farzad Khalvati, Patrik Rogalla ·

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

    arXiv: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-…

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