Key result
AI machine learning algorithms predict cardiovascular disease in African patients with up to ~98% accuracy.
Why the study?
CVD causes over 1 million deaths annually in Africa, where rising prevalence poses prevention, diagnosis, and management challenges, creating a need to explore AI as an innovative solution.
AI and machine learning offer transformative potential for cardiovascular care in Africa, but implementation requires overcoming significant infrastructural, financial, and regulatory challenges.
Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality in Africa, accounting for over 1 million deaths annually. As CVD prevalence rises, Africa faces challenges in prevention, diagnosis, and management. Addressing this crisis requires innovative approaches, and artificial intelligence (AI) has emerged as a transformative solution. Studies already show how machine learning (ML) algorithms can predict various CVDs from patients’ data with accuracy of 73.8%–97.7%. This review explores the potential of AI to improve African cardiovascular care while discussing opportunities, challenges, and pathways for effective implementation. Hence, a comprehensive literature review was conducted using PubMed/MEDLINE, Google Scholar, Africa Journals Online (AJOL), and other online publications and grey literature relevant to the topic. This study discusses opportunities offered by AI to revolutionize cardiovascular care and improve diagnostic accuracy to include predictive analytics, ML, and telemedicine to process structured and unstructured data from m‐Health applications, wearable devices, and hospital records. Moreover, advanced applications could include genome‐wide association studies (GWAS) and precision medicine. Despite its advantages, AI integration faces challenges, including inadequate infrastructure, high implementation costs, policy and funding constraints, as well as limited digital literacy among healthcare providers. Data privacy concerns also remain critical, with only 36 of 55 African countries enacting data protection laws. Pathways to overcome these barriers include Africa's development of ethical standards for data use, investment in workforce training, collaborative partnerships, better funding structure, and strengthening of healthcare infrastructure and research.
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Fatokun et al. (2026) studied this question. AI machine learning algorithms predict cardiovascular diseases in African patients with accuracy between 73.8% and 97.7%, offering potential to improve care.
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