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Deep neural networks excel in medical imaging but remain prone to biases, leading to fairness gaps across demographic groups. We provide the first systematic exploration of Human-AI alignment and fairness in this domain. Our results show that incorporating human insights consistently reduces fairness gaps and enhances out-of-domain generalization, though excessive alignment can introduce performance trade-offs, emphasizing the need for calibrated strategies. These findings highlight Human-AI alignment as a promising approach for developing fair, robust, and generalizable medical AI systems, striking a balance between expert guidance and automated efficiency. The code is available at https://github.com/Roypic/Aligner .

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On the Interplay of Human-AI Alignment, Fairness, and Performance Trade-Offs in Medical Imaging

  • 会议论文

  • 发表日期：

    2026年

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  • Haozhe Luo,
  • Ziyu Zhou,
  • Shelley Zixin Shu,
  • Aurélie Pahud de Mortanges,
  • Robert Berke,
  • Mauricio Reyes

摘要

Deep neural networks excel in medical imaging but remain prone to biases, leading to fairness gaps across demographic groups. We provide the first systematic exploration of Human-AI alignment and fairness in this domain. Our results show that incorporating human insights consistently reduces fairness gaps and enhances out-of-domain generalization, though excessive alignment can introduce performance trade-offs, emphasizing the need for calibrated strategies. These findings highlight Human-AI alignment as a promising approach for developing fair, robust, and generalizable medical AI systems, striking a balance between expert guidance and automated efficiency. The code is available at https://github.com/Roypic/Aligner .