Mitigating Domain Shift and Dataset Bias in Deep Learning Models for Cancer Diagnostics

The translation of deep learning architectures from controlled experimental settings into real-world cancer diagnostics is heavily bottlenecked by domain shift and demographic bias. While models routinely achieve near-perfect retrospective performance on internal datasets, validation across external multicenter cohorts incurs a systemic and severe performance degradation, with an average metric drop of 0.16 AUC. This review provides a comprehensive metasynthesis of contemporary algorithmic mitigation paradigms designed to alleviate distribution discrepancies, evaluating techniques ranging from classical stain normalization and adversarial feature alignment to test-time adaptation and large-scale self-supervised pathology foundation models. Our analysis reveals a critical dichotomy between raw diagnostic performance and algorithmic equity: current optimization frameworks that successfully elevate aggregate macro metrics frequently do so by obscuring or actively exacerbating underlying demographic and subpopulation disparities. To achieve safe, equitable, and universally reliable clinical artificial intelligence, the field must transcend static validation habits. We outline a future roadmap anchored on three critical axes: establishing intersectional disparity indices as primary regulatory endpoints, standardizing multivendor metadata-rich benchmarks, and prioritizing.