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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
Published on: September 26, 2018
Artificial intelligence: transforming cardiovascular healthcare in Africa
Patrick Ashinze1, Eniola Akande2, Chukwu Bethrand2
1Faculty of Clinical Sciences, University of Ilorin, Ilorin, Nigeria. patrickashinze@yahoo.com.
Background:
Cardiovascular diseases (CVDs), a significant global health concern, are responsible for 13% of all deaths particularly in Africa, where they contribute substantially to the global disease burden, taking several millions of lives globally and annually. Despite advancements in healthcare, the burden of CVDs continues to rise steadily. This comprehensive review critically examines the intersection of artificial intelligence (AI) and cardiovascular disease (CVD) management in Africa. Drawing on a diverse gamut of scholarly literature and empirical evidence, the review assesses the prevalence, impact, and challenges of CVDs in the African context.
Main Body:
The review highlights the potential of AI technologies to revolutionize CVD care, offering insights into its applications in diagnosis, treatment optimization, and remote patient monitoring. It explores existing literature sourced from databases like PUBMED, Scopus and Google Scholar about the current state of AI implementation in African healthcare systems, which are majorly resource-constrained, discussing successes, limitations, and future prospects. The work includes the prevalence and impact of CVDs in Africa, noting the significant public health burden and economic implications. Current challenges in addressing CVDs are outlined, focusing on resource constraints, healthcare system challenges, and socioeconomic factors. Our review takes a dive into AI's role in healthcare, emphasizing its capabilities in disease diagnosis, treatment optimization, and patient monitoring, and presents current applications and case studies of AI in African cardiovascular healthcare. It also addresses the challenges and limitations of implementing AI in this context, such as inadequate infrastructure, lack of high-quality data, and the need for regulatory frameworks.
Conclusion:
Our review emphasizes the urgent need for collaborative efforts among policymakers, healthcare providers, and researchers to overcome barriers to AI integration and ensure equitable access to innovative healthcare solutions. By fetching existing research and offering practical recommendations, this review contributes to the academic discourse on AI-driven healthcare interventions in Africa, offering an understanding of the opportunities and challenges in leveraging technology to address pressing public health concerns. It calls for increased research, investment, and collaboration to harness AI's full potential in transforming cardiovascular healthcare in Africa.
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