Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
Bioelectrochemistry (Amsterdam, Netherlands)
From data to diagnosis: A comprehensive review of machine learning-driven wearable sensors in healthcare.
The integration of machine learning (ML) with advanced wearable sensor technologies is revolutionizing healthcare by enabling real-time, intelligent monitoring of physiological parameters such as electrocardiogram (ECG), blood glucose, and respiratory patterns. This review systematically examines the transformative potential of ML-driven biosensors across three core domains: health monitoring, early disease detection, and precision medicine. Key technological advancements-including self-optimizing sensor networks, explainable AI (XAI) architectures, and edge-computing-enabled miniaturized devices-are critically evaluated. Despite rapid progress, the translation of these technologies into clinical practice faces significant challenges, such as data standardization, algorithmic interpretability, privacy concerns, and regulatory hurdles. This paper also discusses emerging trends, including federated learning, quantum machine learning, and neural interfaces, which hold promise for overcoming these barriers. By addressing these challenges and leveraging ongoing interdisciplinary collaborations, ML-enhanced wearable systems are poised to redefine personalized medicine and proactive healthcare delivery on a global scale.
2 Aug 2026
Read appraisal →ACS nano
Learning Human-Environment Interactions via Wearable AI Interfaces
Wearable artificial intelligence (AI) interfaces are reshaping the boundaries between humans and the environment. While prior works often focus on narrow human-machine interactions, this review proposes an intact interaction information flow. It introduces a comprehensive interaction blueprint spanning local interaction and global interaction to the interaction entity, showing how humans interact with the environment. This review first examines advances in wearable form factors, sensing performance improvement strategies, and data analysis. Special emphasis is onspot on how AI interprets heterogeneous data from tactile signatures for local interaction, wearable vision for global interaction with human motion, and electrophysiological signals for the interaction entity. We then discuss the essential applications of this interaction framework, such as human-machine interaction and smart healthcare. By discussing potential barriers in device reliability, algorithm generalization, and scalable applications of wearable AI interfaces, this review provides an outlook on data-driven inverse sensor design, general intelligence strategies, and building a standard ecosystem for scalable applications. The wearable AI interfaces are toward on-body intelligence, actively perceiving, understanding, and assisting in the complex dynamic human-environment interactions.
19 June 2026
Read appraisal →