Machine Learning for Personalized Medicine in Sub-Saharan Africa: A review

Personalized medicine (PM) has transformed healthcare globally by customizing treatments based on individual characteristics. Sub-Saharan Africa (SSA) faces pressing healthcare challenges, but machine learning can enhance personalized medicine by analyzing complex data efficiently. This technology improves treatment outcomes, diagnostics, and drug discovery. PM can significantly improve treatment outcomes, reduce adverse effects, and enhance diagnostics in SSA, where genetic diversity necessitates such tailored approaches. However, implementing ML in PM faces challenges, including data quality issues, infrastructure deficits, workforce shortages, and limited access to health information technology. Future directions for adopting PM in SSA involve strengthening health data systems, building capacity in data science and AI, and fostering public-private partnerships. Addressing these challenges and leveraging opportunities can improve healthcare outcomes, reduce costs, and advance pharmaceutical research in the region. Machine learning holds immense potential to enhance personalized medicine in SSA, promising a transformative impact on healthcare delivery and patient outcomes.