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Assessing GPT-4 multimodal performance in radiological image analysis.

Dana Brin1,2, Vera Sorin3,4,5, Yiftach Barash3,4,5

  • 1Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel. dannabrin@gmail.com.

European Radiology
|August 30, 2024
PubMed
Summary
This summary is machine-generated.

This study evaluated GPT-4V for interpreting radiological images, finding it accurately identifies imaging modalities but struggles with anatomical regions and pathologies. Current GPT-4V performance is not reliable for clinical use in radiology.

Keywords:
Artificial intelligenceComputed tomography (x-ray)Diagnostic imagingRadiologyUltrasonography

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Radiology and Diagnostic Imaging
  • Computer Vision in Healthcare

Background:

  • Artificial intelligence (AI) shows potential for enhancing diagnostic processes in radiology.
  • Multimodal AI models, like GPT-4V, can analyze both images and text, offering new possibilities for interpreting radiological scans.
  • Assessing the performance of these advanced AI models is crucial for understanding their clinical utility.

Purpose of the Study:

  • To evaluate the performance of the multimodal AI model GPT-4V in interpreting various radiological images.
  • To compare GPT-4V's accuracy in identifying imaging modality, anatomical region, and pathology against senior radiologists.
  • To explore the potential of zero-shot generative AI in radiology diagnostics.

Main Methods:

  • 230 anonymized diagnostic images from emergency room settings were analyzed using GPT-4V.
  • Images included ultrasound (US), computerized tomography (CT), and X-ray modalities.
  • GPT-4V interpretations were compared with those of senior radiologists for accuracy assessment.

Main Results:

  • GPT-4V achieved 100% accuracy in identifying imaging modalities.
  • Anatomical region identification accuracy varied by modality (60.9% for US, 97% for CT, 100% for X-ray).
  • Pathology identification accuracy also varied significantly (9.1% for US, 36.4% for CT, 66.7% for X-ray), with a high diagnostic hallucination rate.

Conclusions:

  • GPT-4V demonstrates potential in radiology but is not yet reliable for clinical interpretation due to inconsistent performance and high hallucination rates.
  • Further development is required to improve GPT-4V's accuracy and reliability for diagnostic purposes in radiology.
  • While promising, GPT-4V cannot be used as a standalone tool in clinical settings at this time.