  Purpose: To analyze a recently published chest radiography foundation model
for the presence of biases that could lead to subgroup performance disparities
across biological sex and race.
  Materials and Methods: This retrospective study used 127,118 chest
radiographs from 42,884 patients (mean age, 63 [SD] 17 years; 23,623 male,
19,261 female) from the CheXpert dataset collected between October 2002 and
July 2017. To determine the presence of bias in features generated by a chest
radiography foundation model and baseline deep learning model, dimensionality
reduction methods together with two-sample Kolmogorov-Smirnov tests were used
to detect distribution shifts across sex and race. A comprehensive disease
detection performance analysis was then performed to associate any biases in
the features to specific disparities in classification performance across
patient subgroups.
  Results: Ten out of twelve pairwise comparisons across biological sex and
race showed statistically significant differences in the studied foundation
model, compared with four significant tests in the baseline model. Significant
differences were found between male and female (P < .001) and Asian and Black
patients (P < .001) in the feature projections that primarily capture disease.
Compared with average model performance across all subgroups, classification
performance on the 'no finding' label dropped between 6.8% and 7.8% for female
patients, and performance in detecting 'pleural effusion' dropped between 10.7%
and 11.6% for Black patients.
  Conclusion: The studied chest radiography foundation model demonstrated
racial and sex-related bias leading to disparate performance across patient
subgroups and may be unsafe for clinical applications.
