Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 7 appraisals
Journal of tissue viability
Clinical decision support tools in wound management: A scoping review of existing and emerging tools.
AIM: This scoping review aimed to identify and map clinical decision support tools (both digital and non-digital) used by clinicians for wound management in acute, primary, and community health settings globally, with a focus on the use of artificial intelligence, and the barriers and enablers to implementation. MATERIALS AND METHODS: Studies published between January 2015, and August 2024 were identified through systematic searches of Scopus, Embase, MEDLINE, and CINAHL, conducted in accordance with JBI methodology and reported using the PRISMA-ScR guidelines. Eligible studies included quantitative, qualitative, and mixed-methods research examining non-digital and AI-supported clinical decision support tools for wound management across healthcare settings. RESULTS: Seventeen studies were included, two evaluating AI-supported clinical decision support tools. Most clinical decision support tools had structured wound assessment and treatment planning, though evidence of clinical effectiveness was limited, with only one tool fully validated. Adoption by nurses was influenced by experience, trust, training, and workflow integration, with senior nurses less likely to rely on clinical decision support tools. AI-enabled tools, and a convolutional neural network-based model, improved assessment consistency, documentation, and workflow efficiency. Key barriers included concerns about trust, clinical autonomy, and usability. CONCLUSIONS: Both traditional and AI-supported clinical decision support tools are used for chronic wound management across acute, primary, and community care, but evidence of effectiveness and validation remains limited. The absence of experimental studies highlights the need for rigorous evaluation, clinician education, and strategies to support integration of AI-enabled clinical decision support tool into routine practice.
2 Aug 2026
Read appraisal →Nano letters
Regulating the Nano-Bio Interface: Converging Electrochemical and Photonic Biosensing for Wearable Diagnostics
The development of wearable biosensors has accelerated due to the combination of nanomaterials, integrative electronics, and miniaturized transduction mechanisms, enabling continuous monitoring of physiological and environmental markers. Electrochemical and photonic modalities have been shown to exhibit complementary capabilities in wearable applications, with each offering distinct advantages in sensitivity, selectivity, miniaturization, and power efficiency. Beyond single-modality functionality, the next generation of wearable diagnostics integrates electrochemical and optical transduction within the same platforms. In this short review, we examine the physical and functional demands of such convergence in wearable systems, highlighting model electrochemical systems, such as textile-integrated wound monitoring and hormone sensors, and how optical modalities provide orthogonal observables of interfacial and biochemical conditions. We further explore new emerging device architectures that leverage electrochemical and optical interrogation to generate robust, information-rich data sets, supporting long-term, real-world deployment of wearable diagnostic technologies for personalized healthcare and continuous monitoring applications.
16 July 2026
Read appraisal →Advances in skin & wound care
Mobile Health App Needs Among Patients With Diabetic Foot Ulcers in China: A Qualitative Study From the Perceptions of Patients, Caregivers, and Health Care Professionals
OBJECTIVE: To explore the specific needs of patients with diabetic foot ulcers (DFUs), caregivers, and health care professionals (HCPs) for a mobile health (mHealth) app, aiming to inform the design and development of effective mHealth service solutions. METHODS: This descriptive qualitative study was conducted from June to September 2024 in the wound care clinics of 2 local hospitals. Participants included patients with DFUs, caregivers, and HCPs directly involved in their care. Interview data were analyzed, synthesized, and refined using content analysis. RESULTS: Five key themes emerged: the pressing need to implement mHealth app services, convenient and personalized access to information, continuous and specialized health guidance, a multidisciplinary approach to disease management, and free access alongside privacy and legal protections. CONCLUSIONS: This study provides valuable insights for the design and development of an mHealth app for DFU. During the development process, it is essential to consider user needs and ensure the app meets the expectations of patients and related groups for personalized, continuous, and specialized health guidance; free access; privacy protection; and multidisciplinary collaboration. A balance should be struck between convenience and security of the app's features, to encourage user engagement and enhance the app's value and effectiveness.
2 July 2026
Read appraisal →ACS sensors
Lignin-Regulated Amphiphilic Janus Membrane for Eco-Friendly Electronic Skin
Eco-friendly electronic skin fabricated from sustainable natural materials represents a pivotal technology for advancing healthcare and sports-oriented wearable electronics, yet balancing comfort and electronic functionality remains challenging due to inadequate moisture management. Herein, we report an eco-friendly lignin-regulated amphiphilic Janus membrane (LAJM)-based pressure sensor that synergistically integrates unidirectional water transport with exceptional sensing performance. Through sulfonation and fluorination modifications of lignin fractions with distinct hydrophilic and hydrophobic properties, we engineer an electrospun layer with controlled hydrophobic-hydrophilic gradients, enabling unidirectional water transport while maintaining a dry and comfortable microenvironment. Moreover, LAJM demonstrates notable biocompatibility, antibacterial efficacy, and antioxidant properties, positioning it as a promising candidate for intelligent wound care systems. The LAJM-based pressure sensor achieves integrated sensing capabilities through its hierarchical structure, exhibiting a high sensitivity of 23.97 kPa-1 in the 0-20 kPa range, an extended operational range (0-200 kPa), and ultrafast response dynamics (22 ms response/31 ms recovery). These metrics enable robust continuous monitoring of vital signs (e.g., radial pulse waveforms), discrimination of voice patterns, and precise motion trajectory mapping under dynamic conditions. This technological breakthrough in lignin valorization establishes a scalable platform for next-generation breathable epidermal electronics, with transformative implications for AI-driven healthcare diagnostics and adaptive human-machine interfaces.
28 June 2026
Read appraisal →ACS sensors
Flexible Wearable Closed-Loop Diagnostic and Therapeutic System: A New Paradigm for Skin Health Management
As the largest external barrier of the human body, the skin is essential to overall health and quality of life. Conventional skin health management faces limitations such as insufficient real-time monitoring and lack of personalization. Flexible wearable devices have recently emerged as a transformative technology, enabling non-invasive sensing and targeted therapy with high skin compatibility. This review comprehensively summarizes recent advances in flexible wearable closed-loop diagnostic and therapeutic systems for skin health management. It outlines the core technological components, including skin bioinformation collection, biosignal monitoring, signal transmission and data analysis, and targeted intervention. Key applications in physiological and pathological monitoring, wound healing, drug delivery, and physical therapy are discussed. Furthermore, the innovation trajectory of closed-loop systems is clarified, key challenges are analyzed, and targeted solutions are proposed. Future directions are prospected from cross-disciplinary views including digital twins and personalized medicine, establishing a new paradigm for real-time, personalized, and continuous skin health management.
28 June 2026
Read appraisal →Journal of global health
Deep learning-based pressure injury staging: a multicentre study involving 59 hospitals
BACKGROUND: Accurate assessment of pressure injury staging is essential for guiding appropriate care, reducing patient suffering, alleviating the healthcare burden, and improving quality of life. We aimed to develop a deep learning-based model for pressure injury recognition and to translate the best-performing model into a preliminary smartphone application. METHODS: We conducted a multicentre retrospective study using pressure injury images collected from 59 hospitals. We applied to the data set three artificial intelligence-based image analysis models, Mask R-convolutional neural network (CNN) with ResNet-18, Mask R-CNN with Swin Transformer, and Segmenting Objects by Locations, version 2. We assessed model performance using standard evaluation metrics, including precision-recall curves, average recall (AR), average precision (AP), and mean average precision (mAP). The model achieving the best overall performance was selected for subsequent translation into a smartphone-based tool. RESULTS: We included a total of 1903 pressure injury images, with 1713 used for model training and 190 for validation. Among the models evaluated, Mask R-CNN with Swin Transformer demonstrated the highest overall performance (mAP = 0.894, AP50 = 0.900, AP = 0.757, AR100 = 0.792), surpassing the corresponding metrics of the other two models. The model, based on Mask R-CNN with a Swin Transformer, was integrated into a smartphone for preliminary translation. CONCLUSIONS: The proposed deep learning-based system showed promising performance in pressure injury staging and may provide meaningful support for clinical decision-making. Future studies should focus on further validation and refinement using larger and more diverse data sets to enhance their applicability in routine clinical practice.
20 June 2026
Read appraisal →Lasers in medical science
Clinical dosimetry and efficacy of LED photobiomodulation for chronic lower-limb wound healing: a systematic review of randomized trials
Chronic lower-limb wounds represent a major clinical and socioeconomic burden due to delayed healing and high recurrence rates. Light-emitting diode (LED) photobiomodulation has been proposed as a noninvasive and low-cost adjunctive therapy; however, clinical evidence remains inconsistent. This systematic review aimed to synthesize evidence from randomized clinical trials investigating the efficacy of LED photobiomodulation for chronic lower-limb wound healing and to identify irradiation parameters associated with improved outcomes. A comprehensive search was conducted in PubMed, Scopus, Web of Science, and Embase up to May 2026. Six randomized clinical trials met the inclusion criteria. Wavelengths ranged from 620 to 950 nm, and energy densities varied between 2.4 and 126 J/cm². The findings suggest that LED photobiomodulation may promote wound area reduction, improve wound bed quality, and increase microcirculation, particularly in diabetic foot ulcers. However, one study using a high energy density (126 J/cm²) did not demonstrate beneficial effects, suggesting a possible dose-dependent response. The overall certainty of the evidence, assessed using the GRADE approach, was classified as very low due to inconsistency and indirectness among the included studies. Although LED photobiomodulation appears to be safe and demonstrates therapeutic potential, substantial heterogeneity in irradiation parameters, small sample sizes, and methodological limitations preclude definitive conclusions regarding its clinical efficacy. Well-designed randomized controlled trials with standardized protocols and dose-response investigations are needed to establish optimal therapeutic parameters and confirm clinical efficacy.
20 June 2026
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