Why the study?
Hypertension remains the largest modifiable cause of global mortality, and transformative solutions are needed to overcome roadblocks impeding the implementation of nascent artificial intelligence technology in clinical care.
Artificial intelligence and machine learning have the potential to transform hypertension management, but require rigorous validation and clinical expertise to overcome current implementation roadblocks.
AI/ML in hypertension needs prospective validation; leaves open clinical adoption until efficacy and safety are confirmed.
Hypertension remains the largest modifiable cause of mortality worldwide despite the availability of effective medications and sustained research efforts over the past 100 years. Hypertension requires transformative solutions that can help reduce the global burden of the disease. Artificial intelligence and machine learning, which have made a substantial impact on our everyday lives over the last decade may be the route to this transformation. However, artificial intelligence in health care is still in its nascent stages and realizing its potential requires numerous challenges to be overcome. In this review, we provide a clinician-centric perspective on artificial intelligence and machine learning as applied to medicine and hypertension. We focus on the main roadblocks impeding implementation of this technology in clinical care and describe efforts driving potential solutions. At the juncture, there is a critical requirement for clinical and scientific expertise to work in tandem with algorithmic innovation followed by rigorous validation and scrutiny to realize the promise of artificial intelligence-enabled health care for hypertension and other chronic diseases.
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Padmanabhan et al. (2021) studied this question.
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