AI models for medical diagnosis often exhibit uneven performance across patient populations due to heterogeneity in disease prevalence, imaging appearance, and clinical risk profiles. Existing algorithmic fairness approaches typically seek to reduce such disparities by suppressing sensitive attributes. However, in medical settings these attributes often carry essential diagnostic information, and removing them can degrade accuracy and reliability, particularly in high-stakes applications. In contrast, clinical decision making explicitly incorporates patient context when interpreting diagnostic evidence, suggesting a different design direction for subgroup-aware models. In this paper, we introduce HyperAdapt, a patient-conditioned adaptation framework that improves subgroup reliability while maintaining a shared diagnostic model. Clinically relevant attributes such as age and sex are encoded into a compact embedding and used to condition a hypernetwork-style module, which generates small residual modulation parameters for selected layers of a shared backbone. This design preserves the general medical knowledge learned by the backbone while enabling targeted adjustments that reflect patient-specific variability. To ensure efficiency and robustness, adaptations are constrained through low-rank and bottlenecked parameterizations, limiting both model complexity and computational overhead. Experiments across multiple public medical imaging benchmarks demonstrate that the proposed approach consistently improves subgroup-level performance without sacrificing overall accuracy. On the PAD-UFES-20 dataset, our method outperforms the strongest competing baseline by 4.1% in recall and 4.4% in F1 score, with larger gains observed for underrepresented patient populations.

翻译：医学诊断AI模型常因疾病流行率、影像表现及临床风险特征的异质性而在不同患者群体中表现出不均衡的性能。现有算法公平性方法通常通过抑制敏感属性来减少此类差异。然而在医疗场景中，这些属性往往携带关键诊断信息，移除它们可能降低准确性与可靠性，特别是在高风险应用中。相比之下，临床决策在解读诊断证据时会显式纳入患者背景信息，这为亚群感知模型提供了新的设计思路。本文提出HyperAdapt框架——一种患者条件化自适应机制，在保持共享诊断模型的同时提升亚群可靠性。年龄、性别等临床相关属性被编码为紧凑嵌入向量，用于调节超网络风格模块，该模块为共享主干网络的选定层生成小型残差调制参数。该设计既保留了主干网络学习的通用医学知识，又能实现反映患者特异性变异的有针对性调整。为确保效率与鲁棒性，自适应过程通过低秩瓶颈参数化进行约束，同时限制模型复杂度与计算开销。在多个公共医学影像基准测试中的实验表明，所提方法能在不牺牲整体准确性的前提下持续提升亚群层面性能。在PAD-UFES-20数据集上，本方法在召回率与F1分数上分别超越最强基线4.1%与4.4%，且在代表性不足的患者群体中观察到更显著的性能提升。