Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review

Abstract

The rapid development of biosensing technologies together with the advent of deep learning has marked an era in healthcare and biomedical research where widespread devices like smartphones, smartwatches, and health-specific technologies have the potential to facilitate remote and accessible diagnosis, monitoring, and adaptive therapy in a naturalistic environment. This systematic review focuses on the impact of combining multiple biosensing techniques with deep learning algorithms and the application of these models to healthcare. We explore the key areas that researchers and engineers must consider when developing a deep learning model for biosensing: the data modality, the model architecture, and the real-world use case for the model. We also discuss key ongoing challenges and potential future directions for research in this field. We aim to provide useful insights for researchers who seek to use intelligent biosensing to advance precision healthcare.

Advancing the Frontier: Neuroimaging Techniques in the Early Detection and Management of Neurodegenerative Diseases

“…This approach employs wearable and non-wearable sensor devices to monitor physiological activities and behaviors, including sleep cycles, cognitive changes, and motor activity. The continuous, real-time data provided by these devices are analyzed by AI algorithms to identify early warning signs of disease, offering a more dynamic and accurate method to track disease progression [ 44 ].…”