Artificial intelligence demonstrates potential to improve hypertension care through enhanced risk prediction and monitoring, though challenges in algorithmic bias, interpretability, and real-world implementation remain.
Hypertension remains one of the most important modifiable risk factors for cardiovascular disease worldwide, yet blood pressure control rates remain suboptimal despite advances in diagnosis and treatment. Artificial intelligence (AI) has emerged as a promising tool to improve hypertension care through enhanced risk prediction, continuous monitoring, and clinical decision support. Enthusiasm surrounding AI must be balanced against challenges related to algorithmic bias, model interpretability, data quality, and clinical implementation. While AI has the potential to transform hypertension management, its greatest challenge is no longer predictive accuracy but successful integration into real-world clinical practice. This editorial discusses the opportunities and limitations of AI in hypertension care and argues that implementation science, transparency, and equitable deployment should become the next priorities for the field.
Ghantiwala et al. (Thu,) conducted a editorial in Hypertension. Artificial Intelligence was evaluated. Artificial intelligence demonstrates potential to improve hypertension care through enhanced risk prediction and monitoring, though challenges in algorithmic bias, interpretability, and real-world implementation remain.