# Digital therapeutics into geriatric cardiovascular emergency care

## TL;DR

This paper explores how digital therapeutics and AI can improve emergency cardiovascular care for elderly patients.

## Contribution

The paper highlights novel applications of AI biosensing, VR, and machine learning in geriatric cardiovascular emergency care.

## Key findings

- AI biosensing improves arrhythmia detection sensitivity compared to conventional monitoring.
- Deep learning models outperform traditional methods in predicting cardiovascular events.
- VR-assisted cardiac rehabilitation reduces anxiety scores in elderly patients.

## Abstract

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.