Source: STAT
Women's underdiagnosis and dismissal in clinical settings mean the medical records used to train AI diagnostic systems encode existing gender biases at scale—perpetuating rather than correcting them. AI trained on incomplete female health data will likely continue to miss conditions disproportionately affecting women, from autoimmune diseases to cardiac events. Digitizing biased medical records and then automating decisions based on them gives institutional bias computational permanence.