ReviewPublic health challenges2026
Artificial Intelligence in African Cardiovascular Care: Opportunities, Challenges, and Pathways to Improved Outcomes.
Review in Public health challenges, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
8 authors.
Funding
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Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.