BACKGROUND
Artificial Intelligence (AI) has revolutionized healthcare delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions.
OBJECTIVE
Currently, little is known about the use of AI-powered mHealth settings. Therefore, this scoping review aims to map current research on the emerging use of AI-powered mHealth (AIM) for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for healthcare delivery in the last two years.
METHODS
Using Arksey and O’Malley’s 5-point framework for conducting scoping reviews, we review AIM literature from the past two years in the fields of Biomedical Technology, AI, and Information Systems (IS). We searched three databases - informs PubsOnline, e-journal archive at MIS Quarterly, and ACM Digital Library using keywords such as mobile healthcare, wearable medical sensors, smartphones and AI. We include AIM articles and exclude technical articles focused only on AI models. Also, we use the PRISMA technique for identifying articles that represent a comprehensive view of current research in the AIM domain.
RESULTS
We screened 108 articles focusing on developing AIM models for ensuring better healthcare delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion. A majority of the articles were published last year (31/37). In the selected articles, AI models were used to detect serious mental health issues such as depression and suicidal tendencies and chronic health conditions such as sleep apnea and diabetes. The articles also discussed the application of AIM models for remote patient monitoring and disease management. The primary health concerns addressed relate to three categories: mental health, physical health, and health promotion & wellness. Of these, AIM applications were majorly used to research physical health, representing 46% of the total studies. Finally, a majority of studies use proprietary datasets (28/37) rather than public datasets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available datasets for AIM research.
CONCLUSIONS
The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the healthcare domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques such as Federated Learning (FL) and Explainable AI (XAI) can act as a catalyst to increase the adoption of AIM and enable secure data sharing across the healthcare industry.
Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data
