With AI becoming more prevalent in medical practice, Dhruv Khullar, MD, a physician at New York-Presbyterian Hospital and an assistant professor at Weill Cornell Medicine, detailed how AI is a contributor to the worsening of health disparities in a New York Times opinion piece.
Chiefly, training is a problem in AI. The technology must diagnose disease on large data sets, with patients from diverse backgrounds. If data from one particular background is not included, it may skew the results, Khullar noted. Additionally, medical research does not include enough women and minority populations, which may also distort findings.
AI also perpetuates the economic and social biases that contribute to health disparities—especially in situations where there is a high degree of uncertainty. For example, Khullar wrote, if patients with lower social economic status do worse after organ transplantation or receiving chemotherapy for end-stage cancer, AI may determine that these patients will not benefit from further treatment.
“American healthcare has always struggled with income- and race-based inequities rooted in various forms of bias,” Khullar wrote. “The risk with AI is that these biases become automated and invisible—that we begin to accept the wisdom of machines over the wisdom of our own clinical and moral intuition.”
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