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GPT-4V shows promise in text-based radiology questions but struggles with image interpretation, highlighting a need for further development in visual diagnostic capabilities for artificial intelligence in medicine.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Radiology Education

Background:

  • Advancements in large language models (LLMs) like ChatGPT (generative pretrained transformer) offer new potential in radiology.
  • The baseline performance of LLMs in radiology-specific tasks remains understudied.
  • GPT-4 with vision (GPT-4V) presents an opportunity to evaluate AI capabilities in medical diagnostics.

Purpose of the Study:

  • To assess GPT-4V's performance on radiology in-training examination questions.
  • To evaluate the model's baseline knowledge in radiology, including image-based and text-only questions.
  • To gauge the potential of AI in supporting radiology education and practice.

Main Methods:

  • GPT-4V (September 2023 release) was tested on 386 retired American College of Radiology Diagnostic Radiology In-Training Examination questions.
  • The assessment included 189 image-based and 197 text-only questions.
  • Subanalyses explored the impact of different zero-shot prompts on performance, with statistical analysis using chi-squared and McNemar tests.

Main Results:

  • GPT-4V answered 65.3% of unique questions correctly (246/377).
  • Accuracy was significantly higher for text-only questions (81.5%) compared to image-based questions (47.8%).
  • Chain-of-thought prompting improved performance on text-based questions, but no prompt differences were noted for image-based questions.

Conclusions:

  • GPT-4V demonstrates competence in text-based radiology knowledge but has significant deficits in interpreting radiologic images.
  • The findings suggest current AI models require further refinement for robust application in medical image analysis.
  • Further research is needed to enhance AI's visual diagnostic capabilities in radiology.