review · OMICS A Journal of Integrative Biology
Artificial intelligence and digital health are increasingly adopted across both resource-rich and resource-limited regions, driven by machine learning, big data, and advanced algorithms. Congenital heart defects in sub-Saharan Africa present a critical area where these technologies can enhance diagnostic performance and precision risk prediction. Across cardiology, digital health tools incorporate complementary methods such as neural networks, deep learning, natural language processing, and embedded digital sensors. These algorithmic approaches begin to complement traditional medical expertise, reshaping standard practices within precision medicine and molecular diagnostics. Drawing from implementation experiences in congenital heart defects, ten core lessons illustrate how digital healthcare systems can be delivered in resource-constrained settings. Effective implementation depends on systems approaches to data capture, analysis, and interpretation, offering valuable insights for both developing and developed nations as digital health continues to evolve globally.
Heart conditions such as congenital heart defects represent a significant medical burden in resource-limited areas like sub-Saharan Africa. Utilising artificial intelligence and digital health can assist medical professionals by strengthening diagnostic accuracy and precision risk prediction. Understanding the practical lessons learned from these deployments helps healthcare planners and innovators design digital health interventions that function effectively within constrained healthcare environments.
The work relates to digital health applications, including neural networks, natural language processing, and embedded sensors for cardiovascular diagnosis and risk prediction. The intended users are medical professionals and healthcare providers operating in resource-limited settings. The abstract presents an expert review evaluating implementation experiences and lessons learned rather than a finished commercial product, indicating the field is actively developing and testing systems-level approaches for data capture and analysis.
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Artificial intelligence (AI) is one of the key drivers of digital health. Digital health and AI applications in medicine and biology are emerging worldwide, not only in resource-rich but also resource-limited regions. AI predates to the mid-20th century, but the current wave of AI builds in part on machine learning (ML), big data, and algorithms that can learn from massive amounts of online user data from patients or healthy persons. There are lessons to be learned from AI applications in different medical specialties and across developed and resource-limited contexts. A case in point is congenital heart defects (CHDs) that continue to plague sub-Saharan Africa, which calls for innovative approaches to improve risk prediction and performance of the available diagnostics. Beyond CHDs, AI in cardiology is a promising context as well. The current suite of digital health applications in CHD and cardiology include complementary technologies such as neural networks, ML, natural language processing and deep learning, not to mention embedded digital sensors. Algorithms that build on these advances are beginning to complement traditional medical expertise while inviting us to redefine the concepts and definitions of expertise in molecular diagnostics and precision medicine. We examine and share here the lessons learned in current attempts to implement AI and digital health in CHD for precision risk prediction and diagnosis in resource-limited settings. These top 10 lessons on AI and digital health summarized in this expert review are relevant broadly beyond CHD in cardiology and medical innovations. As with AI itself that calls for systems approaches to data capture, analysis, and interpretation, both developed and developing countries can usefully learn from their respective experiences as digital health continues to evolve worldwide.
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DOI: 10.1089/omi.2019.0142
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