Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries

Mohamad S. Alabdaljabar, Babar Hasan, Peter A. Noseworthy, Joseph F. Maalouf, Naser M. Ammash, Shahrukh K. Hashmi

Research output: Contribution to journalReview articlepeer-review


Artificial intelligence (AI) and machine learning (ML) is a promising field of cardiovascular medicine. Many AI tools have been shown to be efficacious with a high level of accuracy. Yet, their use in real life is not well established. In the era of health technology and data science, it is crucial to consider how these tools could improve healthcare delivery. This is particularly important in countries with limited resources, such as low- and middle-income countries (LMICs). LMICs have many barriers in the care continuum of cardiovascular diseases (CVD), and big portion of these barriers come from scarcity of resources, mainly financial and human power constraints. AI/ML could potentially improve healthcare delivery if appropriately applied in these countries. Expectedly, the current literature lacks original articles about AI/ML originating from these countries. It is important to start early with a stepwise approach to understand the obstacles these countries face in order to develop AI/ML-based solutions. This could be detrimental to many patients’ lives, in addition to other expected advantages in other sectors, including the economy sector. In this report, we aim to review what is known about AI/ML in cardiovascular medicine, and to discuss how it could benefit LMICs.

Original languageEnglish (US)
Pages (from-to)285-295
Number of pages11
JournalJournal of Multidisciplinary Healthcare
StatePublished - 2023


  • Artificial intelligence
  • Cardiology
  • Countries
  • Income
  • Low
  • Machine learning
  • Middle

ASJC Scopus subject areas

  • General Nursing


Dive into the research topics of 'Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries'. Together they form a unique fingerprint.

Cite this