Evaluating AI-driven precision oncology for breast cancer in low- and middle-income countries: a review of machine learning performance, genomic data use, and clinical feasibility

AI-enabled systems show promise for improving breast cancer treatment planning, especially in early-stage disease and resource-limited settings. However, the evidence base remains dominated by HIC-derived datasets and retrospective analyses, with persistent challenges related to domain shift, data representativeness, and genomic governance. Advancing equitable AI-driven oncology will require prospective multicenter validation, expanded LMIC-based data generation, and context-specific implementation strategies.