Attention guided fair artificial intelligence modeling for skin cancer diagnosis

Artificial intelligence (AI) has shown promise in dermatology, offering accurate and non-invasive diagnosis of skin cancer. While extensive research has addressed skin-tone bias, gender bias in dermatologic AI remains underexplored, potentially perpetuating diagnostic disparities. In this study, we developed LesionAttn, an algorithm designed to mitigate gender bias by directing model attention toward lesions, thereby mirroring clinicians' diagnostic focus. Combined with Pareto Frontier optimization for dual-objective model selection, LesionAttn balances gender fairness and diagnostic performance. Validated on two large-scale dermatologic datasets for binary malignancy classification, LesionAttn significantly mitigated gender bias while maintaining high diagnostic performance, outperforming existing bias-mitigation algorithms. Our study demonstrates that explicitly guiding model attention to medically essential features provides a practical approach to advance both performance and fairness in dermatologic AI. By leveraging clinical priors to bridge the gap between human expertise and algorithmic optimization, this study demonstrates a feasible pathway for developing equitable and reliable diagnostic tools.