Artificial Intelligence in Hypertension: Seeing Through a Glass Darkly
Sandosh Padmanabhan1, Tran Quoc Bao Tran1, Anna F Dominiczak1
1BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow.
Circulation Research
|April 1, 2021
Summary
Artificial intelligence (AI) and machine learning (ML) offer transformative potential for managing hypertension, a leading cause of global mortality. Overcoming implementation challenges requires collaboration between clinicians and data scientists for AI-driven healthcare solutions.
Area of Science:
- Medical Informatics
- Cardiovascular Medicine
- Artificial Intelligence in Healthcare
Background:
- Hypertension is a major modifiable risk factor for global mortality, persisting despite extensive research and available treatments.
- Current approaches to hypertension management necessitate innovative solutions to mitigate its widespread impact.
Purpose of the Study:
- To review the application of artificial intelligence (AI) and machine learning (ML) in medicine, with a specific focus on hypertension.
- To identify key challenges hindering the clinical implementation of AI/ML in healthcare.
- To highlight ongoing efforts and potential solutions for integrating AI/ML into hypertension management.
Main Methods:
- Clinician-centric review of AI and ML applications in medical practice.
- Analysis of existing literature on AI/ML in hypertension research and clinical settings.
- Identification of implementation barriers and proposed solutions.
Main Results:
- AI and ML present promising avenues for transforming hypertension care and reducing disease burden.
- Significant obstacles impede the widespread adoption of AI/ML in clinical practice.
- Collaboration between clinical expertise and data science innovation is crucial for progress.
Conclusions:
- Realizing the potential of AI-enabled healthcare for hypertension requires overcoming current implementation hurdles.
- Rigorous validation and scrutiny are essential for the safe and effective deployment of AI/ML tools.
- Interdisciplinary collaboration is key to advancing AI in managing hypertension and other chronic diseases.
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