ArticleInternational wound journal2025
Assessing Clinician Consistency in Wound Tissue Classification and the Value of AI-Assisted Quantification: A Cross-Sectional Study.
Article in International wound journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Automated Segmentation of Wound Bed Tissue from Clinical Photographs: A Benchmark of Five Deep Learning Architectures Under a Shared Protocol.Journal of imaging informatics in medicine · 2026Article
- Validation of a Clinical Decision-Support Algorithm for Chronic Wound Classification and Treatment: An Expert Consensus.International wound journal · 2026Article
- Accurate AI-Based Characterization of Wound Size and Tissue Composition in Hard-to-Heal Wounds.Journal of clinical medicine · 2025Article
- Detection of Hypergranulation Tissue in Chronic Wound Images Using Artificial Intelligence Algorithms.Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair SocietyArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study investigated the relationship between clinician assessments and the AI-generated scores, highlighting how correlations vary based on clinician expertise. It also explored the proportion of tissue types identified by clinicians relative to AI assessments and assess the inter-clinician agreement in quantifying tissue types, identifying variations based on clinician experience. A cross-sectional survey used purposive, non-random sampling to recruit 50 wound care clinicians. Participants reported their specialisation and experience level before identifying and quantifying granulation, slough, eschar, and epithelialisation in nine wound images. An AI model analysed the same images for comparison. Experienced clinicians and wound care specialists reported higher confidence in assessments. Inter-clinician agreement was moderate-good for granulation and slough (ICC: 0.763-0.762) and moderate-excellent for eschar (ICC: 0.910), but moderate-poor for epithelialisation (ICC: 0.435). Clinicians strongly correlated with AI for granulation, slough, and eschar (r = 0.879, 0.955 and 0.984, respectively). Epithelialisation was more challenging, with a 60% identification rate and moderate correlation with AI (r = 0.579). AI-generated scores aligned with clinician assessments for granulation, slough, and eschar. However, epithelialisation, which is crucial for objectively measuring healing progress, showed greater variability, suggesting that AI could improve the reliability of its assessment, potentially leading to more consistent wound evaluation to guide treatment decisions.
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