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Natural language processing (NLP) tools for radiology report annotation are accurate overall but show significant demographic bias, particularly in older patients. Further research is needed to improve fairness in AI.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Radiology
  • Natural Language Processing

Background:

  • Natural language processing (NLP) is widely used for annotating radiology datasets to train deep learning (DL) models.
  • The accuracy and potential demographic biases of these NLP methods, especially across diverse patient groups, require thorough investigation.
  • Understanding these limitations is crucial for developing equitable AI in medical imaging.

Purpose of the Study:

  • To evaluate the accuracy of four NLP radiology report labeling tools.
  • To assess demographic bias in these NLP tools across different patient groups.
  • To compare performance on two distinct chest radiograph datasets.

Main Methods:

  • Retrospective study analyzing chest radiograph reports from MIMIC (n=692) and IU (n=3665) datasets (April 2022-April 2024).
  • Four NLP tools (CheXpert, RadReportAnnotator, GPT-4, cTAKES) were evaluated against radiologist annotations for 14 thoracic disease labels.
  • Performance metrics included accuracy and error rates; bias was assessed using Pearson chi-squared tests across demographic subgroups.

Main Results:

  • All four NLP tools demonstrated high overall accuracy on both datasets.
  • Significant differences (P < .001) in error rates were observed across age groups for most tools.
  • Poorer performance was noted in older patients (e.g., >80 years) for CheXpert, RRA, and cTAKES, and in the 60-80 age group for GPT-4.

Conclusions:

  • While NLP tools for radiology report annotation are accurate in aggregate, significant demographic bias exists.
  • Older patient populations are disproportionately affected by poorer NLP performance.
  • Addressing these biases is essential for the responsible implementation of AI in radiology.