Integrating Artificial Intelligence Into Digital Health-Optimized Therapeutics for the Remote Cardiac Rehabilitation

Abstract

Cardiovascular diseases (CVDs), which have high morbidity and mortality, have become one of the world’s largest public health concerns. Although primary hospitalization can partially relieve symptoms, many patients continue to have poor prognoses and lowered quality of life after discharge. Cardiac rehabilitation (CR) is an important recommendation for patients undergoing cardiac surgery, as well as those who suffer from severe cardiovascular events or chronic cardiac disease. However, standard, center-based CR options tend to be ineffective and hard to access, leading to low compliance. The development of digital health technologies (DHTs), especially wearables—which can be combined with mobile applications-has enabled home-based CR, that is, remote CR. This model has substantially enhanced CR efficiency and patient adherence. With advancements in artificial intelligence (AI), including machine learning (ML) algorithms and deep learning, large-scale data from wearables and other DHTs can be effectively retrieved and interpreted. Further incorporation of AI into DHTs may provide real-time fitness telemonitoring, accurate risk recognition and prediction, individualized exercise recommendations, and improved patient adherence. In this review, we succinctly highlight several applications of both AI and wearables in remote CR, and evaluate their collective roles in patient CR execution from several perspectives. We hope our review will help advance further integration of AI and DHT into home-based CR, and plan to direct future research toward refining the use of AI in new-era digital CR.