Emerging Artificial Intelligence-Empowered mHealth: Scoping Review

No prior work had resolved the full scope of how mobile health integrates with advanced computational intelligence. That uncertainty drove this investigation into current trends within the field. Prior research has shown that machine learning algorithms can improve diagnostic accuracy for various illnesses. However, the specific landscape of mobile-based intelligent systems remains fragmented and poorly understood. This gap motivated a systematic mapping of existing literature to clarify how these tools function. Researchers have previously explored individual applications without synthesizing the broader technological ecosystem. Understanding these developments is vital for advancing modern clinical workflows. This review addresses the lack of comprehensive knowledge regarding the current state of these digital health platforms.

The review aims to map current research regarding the emerging use of intelligent mobile systems for health management. This investigation seeks to synthesize findings from models increasingly utilized for clinical delivery over the past two years. The authors identify a gap in existing knowledge concerning the practical application of these integrated technologies. By examining recent literature, the study clarifies how predictive models leverage data from wearable devices. The primary objective involves categorizing these applications into mental health, physical health, and wellness promotion. The researchers intend to highlight both the potential and the current limitations of this growing domain. This work is motivated by the rising demand for remote disease management observed during the pandemic. Ultimately, the authors provide a structured overview to guide future developments in the integration of intelligent systems within medicine.

The authors performed a systematic mapping of literature published within the last two years. They utilized the Arksey and O'Malley five-point framework to guide the investigation process. Three distinct databases were searched, including PubsOnline, the MIS Quarterly archive, and the Association for Computing Machinery library. Search queries incorporated terms related to mobile healthcare, wearable sensors, and smartphones. The team applied the PRISMA technique to identify studies providing a comprehensive view of the domain. Inclusion criteria focused on articles developing models for clinical delivery and disease diagnosis. Technical papers centered exclusively on algorithmic architecture were omitted from the final selection. This approach ensured that only research with clear practical applications for patient management remained in the final set.

The researchers identified 37 eligible articles from an initial screening of 108 publications. A total of 31 studies, representing 76 percent of the included literature, were published within the last year. Fourteen articles focused on physical health, accounting for 38 percent of the total analyzed research. Nine studies investigated models for detecting serious mental health issues and chronic conditions like sleep apnea. Furthermore, 28 of the 37 studies utilized proprietary data sets instead of public information. The authors observed a notable scarcity of research addressing chronic psychological conditions. Additionally, they highlighted a lack of publicly accessible data repositories for this specific field. These findings demonstrate that while the domain is growing, it faces significant challenges regarding data transparency and scope.

The authors propose that intelligent mobile systems represent an expanding field for clinical monitoring. These digital tools offer precise forecasts that facilitate proactive medical interventions on a large scale. The researchers suggest that recent computational methods could accelerate the widespread integration of these technologies. Specifically, federated learning and interpretable algorithms might improve secure information exchange across the medical sector. The synthesis indicates that current efforts prioritize physical health over chronic psychological conditions. Furthermore, the analysis reveals a significant reliance on private information repositories rather than open-access data. The team emphasizes that addressing these limitations is necessary for future progress in the domain. These findings underscore the potential for mobile-based intelligence to transform remote patient care delivery.