Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 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 →BMJ health & care informatics
Clinicians' and patients' views and experiences on virtual hospital care: a systematic review of qualitative evidence.
OBJECTIVES: To appraise and synthesise the literature on patient and clinician experiences with hospital-based virtual care services. METHODS: We searched Medline, Embase, CINAHL and PsycINFO (via Ovid) in October 2023. An updated search was performed in January 2025, with alerts maintained until May 2025. We included qualitative or mixed-methods studies evaluating patient or clinician experiences of hospital-based virtual care. RESULTS: Out of 15 434 identified articles, 19 studies involving 514 clinicians and 215 patients were included. The majority of the studies were from high-income countries. Our analysis identified five themes influencing virtual hospital care experiences: perceived value, confidence and readiness, digital and health literacy, therapeutic relationships, and infrastructure and system support. DISCUSSION: The potential of virtual care services is mediated by a complex relationship between personal, interpersonal and systemic factors. The acceptability of virtual hospital care is highly dependent on tailoring services to meet the individual needs of both patients and clinicians. The findings of this review demonstrate the need to improve clinician capacity and confidence through validated virtual tools and ongoing training. CONCLUSION: To ensure virtual hospital care is successful and equitable, the virtual health model must be co-designed with its end-users. In addition to addressing systemic barriers, emphasis must be placed on refining care delivery to support the needs of diverse patients and professions. Further research, particularly in low- and middle-income countries and through longitudinal studies, is critical for informing future health policy.
31 July 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 →