AI-Driven Checkpoint Inhibitor Response in Precision Oncology

Artificial intelligence (AI) is transforming precision oncology by helping predict responses to immune checkpoint inhibitors (ICIs) into cancers such as non-small cell lung cancer. Biomarkers like tumor mutational burden (TMB) and PD-L1 expression guide treatment decisions, but their predictive value can vary across tumor types and patient populations. AI-driven models that integrate genomic, imaging, and clinical data improve precision, yet high costs, data complexity, and limited implementation restrict widespread adoption. Emerging models using more accessible variables such as cancer type, age, prior therapy, albumin levels, and neutrophil-to-lymphocyte ratio offer practical alternatives that complement TMB-based prediction. In Africa, challenges are heightened by limited biomarker testing, underrepresentation in global genomic datasets, and the risk of algorithmic bias. However, Africa’s substantial genetic diversity offers opportunities to advance understanding of tumor-immune interactions. Building robust local data ecosystems, strengthening computational capacity, and supporting African-led AI innovation will be essential for equitable integration of AI in cancer immunotherapy. These efforts directly align with the Sustainable Development Goals, particularly SDG 3 (Good Health and Well-Being) and SDG 9 (Industry, Innovation, and Infrastructure), ensuring that emerging technologies benefit all populations, including those across Africa.