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  11. Chatgpt-4-driven Liver Ultrasound Radiomics Analysis: Advantages And Drawbacks Compared To Traditional Techniques

ChatGPT-4-Driven Liver Ultrasound Radiomics Analysis: Advantages and Drawbacks Compared to Traditional Techniques

Laith Sultan1, Shyam Sunder B Venkatakrishna1, Sudha Anupindi1

  • 1Children's Hospital of Philadelphia, 734 Schuylkill Ave, Philadelphia, PA 19146, Philadelphia, US.

JMIR AI
|May 19, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

Large language models like ChatGPT-4 show promise in liver ultrasound radiomics, offering faster analysis and automated evaluation for diagnosing liver conditions like fibrosis and steatosis.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiomics

Background:

  • Artificial intelligence (AI) is revolutionizing medical imaging.
  • Large language models (LLMs) like ChatGPT-4 are emerging as tools for automated image interpretation.
  • The application of ChatGPT-4 in liver ultrasound radiomics is largely unexplored.

Purpose of the Study:

  • To evaluate ChatGPT-4's capability in liver ultrasound radiomics.
  • To assess its ability to differentiate fibrosis, steatosis, and normal liver tissue.
  • To compare its performance against conventional image analysis software.

Main Methods:

  • Seventy grayscale ultrasound images from a preclinical liver disease model were analyzed.
  • ChatGPT-4 extracted texture features, compared with Interactive Data Language (IDL).

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  • Statistical analyses (ANOVA, logistic regression) assessed feature significance and diagnostic performance.

    • Main Results:

    • ChatGPT-4 identified nine key textural features with significant differences across liver conditions (p < 0.05).
    • It achieved 76% accuracy and 83% sensitivity in classifying liver disease, with high AUC values for specific conditions (e.g., 0.97 for steatosis).
    • ChatGPT-4 demonstrated 40% faster processing than IDL, indicating high-throughput potential.

    Conclusions:

    • ChatGPT-4 shows high feasibility for ultrasound radiomics, despite slightly lower sensitivity than IDL.
    • It offers faster processing, high-throughput analysis, and automated multi-image evaluation.
    • Further refinement is needed to enhance feature reproducibility and diagnostic accuracy for AI-driven imaging workflows.