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

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.