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Hypertension presents the largest modifiable public health challenge due to its high prevalence, its intimate relationship to cardiovascular diseases, and its complex pathogenesis and pathophysiology. Low awareness of blood pressure elevation and suboptimal hypertension diagnosis serve as the major hurdles in effective hypertension management. Advances in artificial intelligence in hypertension have permitted the integrative analysis of large data sets including omics, clinical (with novel sensor and wearable technologies), health-related, social, behavioral, and environmental sources, and hold transformative potential in achieving large-scale, data-driven approaches toward personalized diagnosis, treatment, and long-term management. However, although the emerging artificial intelligence science may advance the concept of precision hypertension in discovery, drug targeting and development, patient care, and management, its clinical adoption at scale today is lacking. Recognizing that clinical implementation of artificial intelligence-based solutions need evidence generation, this opinion statement examines a clinician-centric perspective of the state-of-art in using artificial intelligence in the management of hypertension and puts forward recommendations toward equitable precision hypertension care.
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http://dx.doi.org/10.1161/HYPERTENSIONAHA.124.22349 | DOI Listing |
J Ultrasound Med
September 2025
Department of Clinical Analysis, Federal University of Santa Catarina (UFSC), Florianópolis, Brazil.
Objectives: To evaluate the performance of artificial intelligence (AI)-based models in predicting elevated neonatal insulin levels through fetal hepatic echotexture analysis.
Methods: This diagnostic accuracy study analyzed ultrasound images of fetal livers from pregnancies between 37 and 42 weeks, including cases with and without gestational diabetes mellitus (GDM). Images were stored in Digital Imaging and Communications in Medicine (DICOM) format, annotated by experts, and converted to segmented masks after quality checks.
Nutr Health
September 2025
Independent researcher, Rome, Italy.
Artificial intelligence (AI) is increasingly applied in nutrition science to support clinical decision-making, prevent diet-related diseases such as obesity and type 2 diabetes, and improve nutrition care in both preventive and therapeutic settings. By analyzing diverse datasets, AI systems can support highly individualized nutritional guidance. We focus on machine learning applications and image recognition tools for dietary assessment and meal planning, highlighting their potential to enhance patient engagement and adherence through mobile apps and real-time feedback.
View Article and Find Full Text PDFJ Empir Res Hum Res Ethics
September 2025
TOBB ETU School of Medicine, History of Medicine and Ethics Department, Ankara, Turkey.
This study investigates how scientists, educators, and ethics committee members in Türkiye perceive the opportunities and risks posed by generative AI and the ethical implications for science and education. This study uses a 22-question survey developed by the EOSC-Future and RDA AIDV Working Group. The responses were gathered from 62 universities across 208 universities in Türkiye, with a completion rate of 98.
View Article and Find Full Text PDFACS Nano
September 2025
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Vagus nerve stimulation (VNS) is a promising therapy for neurological and inflammatory disorders across multiple organ systems. However, conventional rigid interfaces fail to accommodate dynamic mechanical environments, leading to mechanical mismatches, tissue irritation, and unstable long-term interfaces. Although soft neural interfaces address these limitations, maintaining mechanical durability and stable electrical performance remains challenging.
View Article and Find Full Text PDFJ Interv Card Electrophysiol
September 2025
School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Cardiology, University Medical Centre, Technical University of Munich, Munich, Germany.