1Meenakshi Medical College Hospital & Research Institute, Meenakshi Academy of Higher Education and Research
2Department of Research, Meenakshi Academy of Higher Education and Research
3Department of Biochemistry, Meenakshi Ammal Dental College and Hospital, Meenakshi Academy of Higher Education and Research
4Meenakshi College of Arts & Science, Meenakshi Academy of Higher Education and Research
5Meenakshi College of Nursing, Meenakshi Academy of Higher Education and Research
6Meenakshi College of Pharmacy, Meenakshi Academy of Higher Education and Research
Background: Wearable technology has also been improving quickly on the consumer level to clinically relevant biosensing platforms that have the ability to monitor the cardiovascular situation continuously. As artificial intelligence (AI) advances, now such devices have an opportunity to identify early structural, valvular, and ventricular pathologies in advance before the patient begins to experience symptoms.
Objective: To measure the potential of AI-enabled wearable devices in the screening of structural, valvular, and ventricular heart disease, and the diagnosis of wearable devices in relation to traditional clinical procedures.
Method: This is a review of the recent clinical, device-validation, and machine-learning model studies. Special emphasis on such sensor modalities as photoplethysmography, single-lead ECG, seismocardiography, and wearable ultrasound, as well as AI related to deep-learning classifiers, signal-quality improvement, and multimodal data combination was made.
Results: Wearable-derived signals were analyzed with the assistance of AI which showed a high level of accuracy in the identification of left-ventricular dysfunction, aortic stenosis, mitral regurgitation, and early structural abnormalities. The studies indicated better sensitivity with uninterrupted observation and automatic anomalous recognition supporting risk stratification better than intermittent clinic-based evaluations. Multimodal wearable data further integrated contributed to the classification of ventricular dysfunction and valvular disease at an early study.
Conclusion: Interactive wearable technology and AI are a potential next-level of screening of structural and valvular disease. Lasting surveillance and completely automated interpretation can allow earlier diagnosis, a better triage, and more cardiovascular proactive treatment.
Keywords: Wearable technology, structural heart disease, ECG monitoring, cardiac screening, valvular disease.
How to cite this article: Suvaithenamudhan S, Purushothaman I, Anandhi D, Shanthi V, Jayabharathi B, Pugazhendi S. Wearable Technology and AI: Next-Generation Screening for Structural, Valvular and Ventricular Disease. Int J Drug Deliv Technol. 2026;16(10s): 242-247; DOI: 10.25258/ijddt.16.10s.36
Source of support: Nil.
Conflict of interest: None