The limits of fair medical imaging AI in real-world generalization

As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Previous research 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 conducted a thorough investigation into the extent to which medical AI uses 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…

• GA
  Gordon and Betty Moore Foundation
• RS
  Radiological Society of North America

  Award: EIHD2204
• G
  Google
• NH
  National Heart, Lung, and Blood Institute

  Award: R01HL167811
• NI
  National Institute of Biomedical Imaging and Bioengineering

  Awards: 75N92020C00021, 75N92020C00008