Medicine and Health
The limits of fair medical imaging AI in real-world generalization
Y. Yang, H. Zhang, et al.
This study reveals the critical challenges of fairness in medical AI for disease classification across various imaging modalities, highlighting how demographic shortcuts lead to biased predictions. Conducted by Yuzhe Yang, Haoran Zhang, Judy W. Gichoya, Dina Katabi, and Marzyeh Ghassemi, the research uncovers that less demographic attribute encoding in models can yield better performance in diverse clinical settings, emphasizing best practices for equitable AI applications.
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