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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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How Well Do Multi-modal LLMs Interpret CT Scans? An Auto-Evaluation Framework for Analyses.

Qingqing Zhu1, Benjamin Hou2,3, Tejas S Mathai2

  • 1National Center for Biotechnology Information, National Library of Medicine.

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|October 1, 2025
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Summary
This summary is machine-generated.

A new framework, GPTRadScore, evaluates AI for CT scan interpretation. While current models show promise, fine-tuning significantly improves accuracy, addressing data limitations for better radiology AI.

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Area of Science:

  • Medical Imaging AI
  • Radiology Workflow Optimization

Background:

  • Automated CT scan interpretation can reduce radiologist workload.
  • Limited datasets and evaluation standards hinder AI development.

Purpose of the Study:

  • Introduce GPTRadScore, a novel evaluation framework for multi-modal LLMs in radiology.
  • Assess AI models like GPT-4V, Gemini Pro Vision, LLaVA-Med, and RadFM in generating CT scan descriptions.

Main Methods:

  • GPTRadScore uses GPT-4 for decomposition and compares AI-generated descriptions against gold-standard sentences.
  • Accuracy is analyzed for body part, location, and finding type.
  • A clinician-annotated benchmark dataset will be released.

Main Results:

  • GPTRadScore shows high correlation with clinician assessments, outperforming traditional metrics.
  • GPT-4V and Gemini Pro Vision show potential but require improved training data.
  • Fine-tuning RadFM significantly boosted accuracy: location (3.41% to 12.8%), body part (29.12% to 53%), and type (9.24% to 30%).

Conclusions:

  • GPTRadScore offers a robust evaluation method for radiology AI.
  • Model performance is heavily influenced by training data limitations.
  • Fine-tuning demonstrates a viable path to enhance AI accuracy in medical image interpretation.