# Artificial Intelligence in African Cardiovascular Care: Opportunities, Challenges, and Pathways to Improved Outcomes

## TL;DR

This paper reviews how artificial intelligence can improve cardiovascular care in Africa despite challenges like poor infrastructure and limited digital literacy.

## Contribution

The paper provides a comprehensive analysis of AI opportunities and challenges in African cardiovascular care, emphasizing pathways for effective implementation.

## Key findings

- AI and machine learning can predict cardiovascular diseases with accuracy ranging from 73.8% to 97.7%.
- Challenges include inadequate infrastructure, high costs, and limited digital literacy in healthcare.
- Solutions involve ethical data standards, training, partnerships, and infrastructure development.

## 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.

Artificial intelligence can transform cardiovascular care in Africa by improving early diagnosis, enabling personalized treatments, expanding telemedicine use, and improving rural healthcare access. Integrating AI‐driven tools with supportive policies, collaborative frameworks, training, funding, and infrastructure can overcome existing challenges and enhance African cardiovascular care.