Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology
Date
2020-05-07
Journal Title
Journal ISSN
Volume Title
Publisher
OMICS A Journal of Integrative Biology
Abstract
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 resourcelimited
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.
Description
Research Article
Keywords
artificial intelligence,, digital health,, machine learning,, deep learning,, congenital heart defects, eHealth