AI-enhanced biosensing increased atrial fibrillation detection by 3.0%, AI portable ultrasound achieved over 85% diagnostic accuracy, VR-assisted cardiac rehabilitation significantly reduced anxiety, and machine learning models predicted 30-day readmission with AUC up to 0.91, improving medication adherence in elderly cardiovascular emergency care.
Digital therapeutics and AI offer promising advancements in geriatric cardiovascular emergency care, including improved risk stratification and rehabilitation, but require targeted validation to overcome age-related data biases.
This mini review investigates the applications of digital therapeutics (DTx) and artificial intelligence (AI) in geriatric cardiovascular emergency care. Key elements include AI-driven biosensing for real-time risk stratification, blockchain-based secure data interoperability, tele-rehabilitation frameworks, and emerging technologies such as digital twins and brain-computer interfaces. Clinical validations shows that AI-enhanced portable ultrasound systems integrated with virtual reality (VR) optimizes diagnostic protocols and resuscitation workflows, while machine learning models achieve superior accuracy in predicting readmission risks and improving medication adherence. Notable research advances included: (1) Compared with conventional monitoring, AI biosensing improved arrhythmia detection sensitivity; (2) Deep learning models were superior to traditional methods in predicting cardiovascular events; (3) VR-assisted cardiac rehabilitation reduced anxiety scores; (4) The predictive readmission algorithm achieved high accuracy through frailty-comorbidity integration; (5) chatbot guided intervention improved medication adherence. However, limitations remain in this field, particularly in addressing age-related data biases and ethical challenges surrounding algorithmic transparency. Future researches should prioritize developing adaptive interfaces for elderly users, and advancing biocybernetic human-machine interfaces capable of stabilizing autonomic dysregulation. Importantly, these innovations must be validated in conjunction with geriatrics to ensure equitable implementation across diverse older populations.
Hu et al. (Tue,) conducted a review in Geriatric cardiovascular emergency care. AI-enhanced digital therapeutics vs. Conventional monitoring or standard care was evaluated on Primary cardiovascular-related outcomes including arrhythmia detection sensitivity, diagnostic accuracy, anxiety reduction, medication adherence, and readmission prediction accuracy. AI-enhanced biosensing increased atrial fibrillation detection by 3.0%, AI portable ultrasound achieved over 85% diagnostic accuracy, VR-assisted cardiac rehabilitation significantly reduced anxiety, and machine learning models predicted 30-day readmission with AUC up to 0.91, improving medication adherence in elderly cardiovascular emergency care.