Keywords
Disease Tracking
Care Coordination
Patient Education
Automating Radiology
How to Cite
Abstract
Chronic diseases, which represent long-term and slow-progressing illnesses such as diabetes and hypertension, are rapidly emerging as a significant public health issue. Ultimately, it is estimated that the prevalence of chronic diseases is set to increase, reaching 57% by 2020. Subsequently, this increase in illness will lead to a considerable burden on health systems, particularly with chronic diseases currently accounting for the majority of health care expenditure, totaling $241 billion in the United States alone. Traditional approaches to slowing the progression of chronic diseases are limited in their capacity to foster patient engagement and are inefficacious for reducing costs related to chronic care complications. Thus, in the digital era of health care, innovative solutions are needed to enhance patient-centered chronic disease management. Machine learning is now entering the health care space, touted as the panacea for low predictive powers, the second opinion for rare diseases, and even a cure-all for diagnostic errors. There is great potential for the application of AI-driven platforms in precision medicine tailored to the unique characteristics of several chronic diseases.
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