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

Luis Fabián Salazar-Garcés^1,2*Elizabeth Katalina Morales-Urrutia¹Franklin Hernan Cashabamba¹Xavier Ricardo Proaño¹Lizette Elena Leiva¹

• ¹Universidad Tecnica de Ambato, Ambato, Ecuador
• ²Allergy and Acarology Laboratory, Salvador - Bahia, Brazil

Background: Artificial intelligence (AI) systems are increasingly used to support treatment decision-making in breast cancer, yet their performance and feasibility in low- and middle-income countries (LMICs) remain incompletely defined. Many high-performing models, particularly genomic and multimodal systems trained on The Cancer Genome Atlas (TCGA), raise questions about cross-domain generalizability and equity. Methods: We conducted an AI-assisted scoping review combining Boolean database searches with semantic retrieval tools (Elicit, Semantic Scholar, Connected Papers). From 497 unique records (Supplementary Table 1), 43 studies met inclusion criteria and 34 reported quantitative metrics. Data extraction included study design, AI model type (treatment-recommendation, prognostic, or diagnostic/subtyping), input modalities, and validation strategies. Risk of bias was assessed using a hybrid PROBAST-AI/QUADAS-AI framework (Supplementary Table 2). Results: Treatment-recommendation systems (e.g., WFO, Navya) showed concordance ranges of 67–97% in early-stage settings but markedly lower performance in metastatic disease. Prognostic and multimodal models frequently achieved AUCs of 0.90–0.99. HIC-trained genomic models demonstrated consistent declines during external LMIC validation (e.g., CDK4/6 response model: AUC 0.9956 → 0.9795). LMIC implementations reported reduced time-to-treatment and improved adherence to guidelines, but these gains were constrained by gaps in electronic health records, limited digital pathology, and insufficient local genomic testing capacity. Conclusions: 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.