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arXiv AI By Giovanni dos Santos Franco, Felipe Mahlow, Ellison Fernando Cardoso, Jos\'e Adenaldo Santos Bittencourt, Felipe Fanchini

Responsible Quantum Machine Learning for Medical Imaging: A Fairness Audit Across Sensitive Subgroups

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The study audits fairness in quantum and classical machine‑learning classifiers for medical imaging, focusing on dermatological lesions (Fitzpatrick skin type) and chest X‑rays (age group). Using ResNet‑18 embeddings, the authors train SVM, MLP, QSVM, and QCNN under baseline and mitigation strategies (Equalized Odds regularisation, Gradient Reversal Learning, and both combined). Results show that while quantum models can match aggregate performance, they often exhibit subgroup disparities, and some mitigation approaches reduce fairness without maintaining clinical performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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