Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries.
Mohamad S AlabdaljabarBabar HasanPeter A NoseworthyJoseph F MaaloufNaser M AmmashShahrukh K HashmiPublished in: Journal of multidisciplinary healthcare (2023)
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.
Keyphrases
- artificial intelligence
- machine learning
- big data
- healthcare
- deep learning
- end stage renal disease
- cardiovascular disease
- public health
- endothelial cells
- chronic kidney disease
- prognostic factors
- newly diagnosed
- mental health
- systematic review
- palliative care
- health information
- affordable care act
- electronic health record
- quality improvement
- cardiovascular events
- type diabetes
- peritoneal dialysis
- human health
- cardiac surgery
- risk assessment
- metabolic syndrome
- acute kidney injury
- patient reported
- cardiovascular risk factors