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This summary is machine-generated.

GPT-4o accurately extracts diagnostic labels from radiology reports, enabling competitive multi-label image classification models. Uncertainty in reports did not impact model performance, showcasing AI

Keywords:
Artificial intelligenceLarge language modelsRadiographyUpper extremity

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Area of Science:

  • Artificial Intelligence in Radiology
  • Machine Learning for Medical Imaging
  • Natural Language Processing in Healthcare

Background:

  • Radiology reports contain valuable diagnostic information.
  • Extracting structured data from free-text reports is challenging.
  • AI models require large, accurately labeled datasets for training.

Purpose of the Study:

  • To assess GPT-4o's zero-shot capability in extracting structured diagnostic labels from radiology reports.
  • To evaluate the impact of these extracted labels, including uncertainty, on multi-label image classification performance for musculoskeletal radiographs.

Main Methods:

  • Retrospective analysis of clavicle, elbow, and thumb radiographs.
  • GPT-4o extracted labels (present, absent, uncertain) from anonymized reports.
  • Uncertainty labels were handled inclusively and exclusively for model training.
  • ResNet50 architecture was used for multi-label classification.
  • Performance was validated on internal and external test sets using AUC and other metrics.

Main Results:

  • GPT-4o achieved >98% accuracy in automatic label extraction across test sets.
  • Label-based models demonstrated competitive performance (e.g., elbow AUC=0.80) regardless of uncertainty handling.
  • Models generalized well to external datasets with consistent performance.
  • No significant performance differences were observed across labeling strategies or datasets (p≥0.15).

Conclusions:

  • GPT-4o effectively extracts high-accuracy diagnostic labels from radiology reports.
  • These labels facilitate the training of competitive multi-label image classification models.
  • Handling of uncertainty in extracted labels did not significantly affect model performance.