Background Artificial intelligence (AI) has revolutionized health care 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 (AIM) settings. Therefore, this scoping review aims to map current research on the emerging use of AIM for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for health care delivery in the last 2 years. Methods Using Arksey and O’Malley’s 5-point framework for conducting scoping reviews, we reviewed AIM literature from the past 2 years in the fields of biomedical technology, AI, and information systems. We searched 3 databases, PubsOnline at INFORMS, e-journal archive at MIS Quarterly, and Association for Computing Machinery (ACM) Digital Library using keywords such as “mobile healthcare,” “wearable medical sensors,” “smartphones”, and “AI.” We included AIM articles and excluded technical articles focused only on AI models. We also used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 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 health care delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion, with 31 of the 37 articles being published last year (76%). Of the included articles, 9 studied AI models to detect serious mental health issues, such as depression and suicidal tendencies, and chronic health conditions, such as sleep apnea and diabetes. Several articles discussed the application of AIM models for remote patient monitoring and disease management. The considered primary health concerns belonged to 3 categories: mental health, physical health, and health promotion and wellness. Moreover, 14 of the 37 articles used AIM applications to research physical health, representing 38% of the total studies. Finally, 28 out of the 37 (76%) studies used proprietary data sets rather than public data sets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available data sets 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 health care domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques, such as federated learning and explainable AI, can act as a catalyst for increasing the adoption of AIM and enabling secure data sharing across the health care industry.

Objective To evaluate the test-retest reliability of event-related power changes in the 30–150 Hz gamma frequency range occurring in the first 150 ms after presentation of an auditory stimulus. Methods Repeat intracranial electrocorticographic (ECoG) recordings were performed with 12 epilepsy patients, at ≥ 1-day intervals, using a passive odd-ball paradigm with steady-state tones. Time-frequency matching pursuit analysis was used to quantify changes in gamma-band power relative to pre-stimulus baseline. Test-retest reliability was estimated based on within-subject comparisons (paired t-test, McNemar’s test) and correlations (Spearman rank correlations, intra-class correlations) across sessions, adjusting for within-session variability. Reliability estimates of gamma-band response robustness, spatial concordance, and reproducibility were compared with corresponding measurements from concurrent auditory evoked N1 responses. Results All patients showed increases in gamma-band power, 50–120 ms post-stimulus onset, that were highly robust across recordings, comparable to the evoked N1 responses. Gamma-band responses occurred regardless of patients’ performance on behavioral tests of auditory processing, medication changes, seizure focus, or duration of test-retest interval. Test-retest reproducibility was greatest for the timing of peak power changes in the high-gamma range (65–150 Hz). Reliability of low-gamma responses and evoked N1 responses improved at higher signal-to-noise levels. Conclusions Early cortical auditory gamma-band responses are robust, spatially concordant, and reproducible over time. Significance These test-retest ECoG results confirm the reliability of auditory gamma-band responses, supporting their utility as objective measures of cortical processing in clinical and research studies.

People all over the world were under severe stress and were concerned about their health after a devastating pandemic struck the world in the form of a novel coronavirus disease (COVID-19) in late December 2019. Many nations imposed strict lockdowns and quarantines, causing citizens to maintain social isolation, throwing many companies to a halt. Thousands of people took to Twitter during these challenging circumstances to express their feelings about being caught in the middle of a storm. Twitter witnessed an outpouring of emotions ranging from fear, anger, and sadness associated with the spread of a novel virus that has no known cure, to voices of support and trust for nations’ official response to the pandemic. In studying the emotional response (anger, fear, and sadness) on Twitter about the COVID-19 crisis, we thus see a tale of two crises unfold—choosing health or economy. We capture collective emotions on social media and investigate the patterns and impact of these negative emotions during various stages of the disease outbreak. It also provides crucial insights to health officials and government agencies on communicating crisis information to the public via social media.

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