As artificial intelligence (AI) rapidly approaches human-level performance in
medical imaging, it is crucial that it does not exacerbate or propagate
healthcare disparities. Prior research has established AI's capacity to infer
demographic data from chest X-rays, leading to a key concern: do models using
demographic shortcuts have unfair predictions across subpopulations? In this
study, we conduct a thorough investigation into the extent to which medical AI
utilizes demographic encodings, focusing on potential fairness discrepancies
within both in-distribution training sets and external test sets. Our analysis
covers three key medical imaging disciplines: radiology, dermatology, and
ophthalmology, and incorporates data from six global chest X-ray datasets. We
confirm that medical imaging AI leverages demographic shortcuts in disease
classification. While correcting shortcuts algorithmically effectively
addresses fairness gaps to create "locally optimal" models within the original
data distribution, this optimality is not true in new test settings.
Surprisingly, we find that models with less encoding of demographic attributes
are often most "globally optimal", exhibiting better fairness during model
evaluation in new test environments. Our work establishes best practices for
medical imaging models which maintain their performance and fairness in
deployments beyond their initial training contexts, underscoring critical
considerations for AI clinical deployments across populations and sites.

人工智能在医学影像学中逼近人类水平的表现，但其是否在不同人群中造成不公平的预测是一个关键问题。本研究对医学影像学中人工智能使用人口统计学编码的程度进行了深入调查，发现在新的测试环境中，较少编码人口统计属性的模型往往表现出更好的公平性。我们的研究为医学影像学模型在超出初始训练环境中保持性能和公平性提供了最佳实践。