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Artificial Intelligence in African Cardiovascular Care: Opportunities, Challenges, and Pathways to Improved Outcomes
Boluwatife Samuel Fatokun1, Omosola Lydia Bolarin2, Ahmed Muhammad Babandi3
1Faculty of Basic Medical Sciences Kwara State University Malete Nigeria.
Insights
Artificial intelligence (AI) can enhance cardiovascular disease (CVD) care in Africa by improving prediction and diagnosis. Overcoming infrastructure and data privacy challenges is key to implementing AI solutions for better health outcomes.
Area of Science:
- Medical Informatics
- Public Health
- Artificial Intelligence in Healthcare
Background:
- Cardiovascular disease (CVD) is a major cause of death in Africa, with over 1 million annual fatalities.
- Rising CVD prevalence in Africa highlights critical gaps in prevention, diagnosis, and management.
- Innovative solutions are urgently needed to address the growing burden of CVD across the continent.
Purpose of the Study:
- To explore the transformative potential of artificial intelligence (AI) in improving cardiovascular care in Africa.
- To review opportunities, challenges, and implementation pathways for AI in African cardiovascular health.
- To assess AI's role in enhancing diagnostic accuracy and patient management for CVD.
Main Methods:
- A comprehensive literature review was conducted using major databases (PubMed/MEDLINE, Google Scholar, AJOL) and grey literature.
- Analysis focused on AI applications, including machine learning (ML), predictive analytics, and telemedicine.
- Consideration was given to data sources such as m-Health, wearables, and electronic health records.
Main Results:
- AI, particularly ML algorithms, demonstrates high accuracy (73.8%-97.7%) in predicting various CVDs.
- AI offers opportunities for predictive analytics, enhanced diagnostics, and personalized medicine (e.g., GWAS).
- Significant challenges include inadequate infrastructure, high costs, policy gaps, and limited digital literacy.
Conclusions:
- AI presents a significant opportunity to revolutionize cardiovascular care and improve health outcomes in Africa.
- Addressing challenges like data privacy (only 36/55 countries have data protection laws) is crucial for AI adoption.
- Effective implementation requires developing ethical standards, investing in training, fostering partnerships, and strengthening infrastructure.
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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