Usability of an Artificial Intelligence-Enhanced Multispectral Imaging Tool for Burn Assessment: A Multi-Centre Retrospective Heuristic Evaluation

Clicks: 17
ID: 316419
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Popular

Ranked #83 of 94 articles by views in journal of burn care & research : official publication of the american burn association

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Accurate burn depth assessment remains difficult. AI-enhanced multispectral imaging (AI-MSI) offers objective, image-based predictions, but evidence on day-to-day usability is limited. The aim of this study is to evaluate the usability of an AI-MSI device in routine practice using a multicentre, modified Nielsen heuristic framework. A retrospective, mixed-methods study was conducted across five UK burn services (September 2024 to August 2025). Doctors and nurses who used the device completed a 12-item Likert survey mapped to six domains and free-text. Outcomes include domain means±SD, medians (IQR), and proportions of positive responses (scores ≥ 4). Mann-Whitney U compared professions; Kruskal-Wallis tested site variability; Dunn pairwise tests with Bonferroni correction were used where indicated. JASP 0.95.1 was used for analysis with statistical significance set at p < 0.05. Forty-nine burn-team members (21 doctors, 28 nurses) participated. Domain means ranged 3.93 to 4.35; medians were ≥ 4.0; positive responses 65.3% to 87.8%. There were no significant differences between professions or centres. Effect sizes were small to moderate; Match & Standards showed the largest effect (ε2 = 0.15) with no pairwise differences after correction. Free-text highlighted speed, clarity of outputs, portability, and workflow fit, and suggested clearer on-screen cues, a lighter imaging head, longer battery life, and streamlined data entry. AI-MSI showed high usability across roles and centres, supporting routine use in clinics, wards, and theatres. As the first multicentre heuristic usability evaluation of AI-MSI in burns, the study provides implementation-focused evidence to guide training, pathway design, and targeted software and hardware refinements.
Reference Key
openalex_W7163864215 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hajar Abdulla, Poh (Leslie) Tan, Sarah E. Bache, Alexandra Murray, Zeeshan Sheikh, Christopher Wearn, Christopher J Lewis
Are you one of these authors? Claim your profile
Journal journal of burn care & research : official publication of the american burn association
Year 2026
DOI
10.1093/jbcr/irag087
URL
https://doi.org/10.1093/jbcr/irag087
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.