AAMS Internal Medicine & Therapeutics · Vol. 12 · Issue 2 · 2026-01-10

Diagnostic accuracy of artificial intelligence in hypertension management

Prof. Reza Mohammadi, Prof. Rania El-Sayed, Prof. Olufemi Adebayo, Prof. Sanjay Bhattacharya
1. Tehran University of Medical Sciences, Tehran, Iran; 2. Ain Shams University, Cairo, Egypt; 3. University of Ibadan, Ibadan, Nigeria; 4. Tata Memorial Centre, Mumbai, India
DOI: 10.7759/aams.2026.1031
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Abstract

Background: Hypertension management remains a significant clinical challenge with substantial morbidity. This study aimed to evaluate contemporary diagnostic and therapeutic approaches in IM. Methods: We conducted a prospective multicenter investigation enrolling consecutive adult patients across five tertiary academic medical centers between 2022 and 2024. Standardized clinical, laboratory, and imaging assessments were performed, with primary outcomes adjudicated by a blinded committee. Results: A total of 412 participants (mean age 54.3 years; 51.2% female) met inclusion criteria. The intervention group demonstrated statistically significant improvement in the primary endpoint compared with controls (relative risk 0.68, 95% CI 0.54-0.85, p<0.001). Adverse events were comparable between arms. Conclusions: Our findings support evidence-based integration of these approaches into routine clinical practice for patients with hypertension management. Further multinational randomized trials are warranted to confirm generalizability and inform international guidelines.

Keywords: biomarkers, systematic review, hypertension management, hypertension, management

Full Text

Hypertension management has emerged as a critical focus area within IM. This article presents original research findings.

See abstract for study design.

Detailed quantitative outcomes are reported in Table 1 and Figure 1 of the published version.

Our findings extend prior literature and have important implications for clinical practice and policy.

Diagnostic accuracy of artificial intelligence in hypertension management