Artificial intelligence in the management and treatment of burns: a systematic review and meta-analyses

Bilal Taib*, Andreas Karwath, K Wensley, Leandro Minku, Georgios Gkoutos, N. S. Moiemen

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

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Introduction and Aim: Artificial Intelligence (AI) is already being successfully employed to aid the interpretation of multiple facets of burns care. In light of the growing influence of AI this systematic review and diagnostic test accuracy meta-analyses aims to appraise and summarise the current direction of research in this field.

Method: A systematic literature review was conducted of relevant studies published between 1990 to 2021 yielding 35 studies. 12 studies were suitable for a Diagnostic Test Meta-Analyses.

Results: The studies generally focussed on burn depth (Accuracy 68.9%-95.4%, Sensitivity 90.8% Specificity 84.4%), burn segmentation (Accuracy 76.0%-99.4%, Sensitivity 97.9% and specificity 97.6%) and burn related mortality (Accuracy >90%-97.5% Sensitivity 92.9% and specificity 93.4%). Neural networks were the most common machine learning algorithm utilised in 69% of the studies. The QUADAS-2 tool identified significant heterogeneity between studies.

Discussion: The potential application of AI in the management of burns patients is promising, especially given its propitious results across a spectrum of dimensions, including burn depth, size, mortality, related sepsis, and acute kidney injuries. The accuracy of the results analysed within this study are comparable to current practices in burns care.

Conclusion: The application of AI in the treatment and management of burns patients, as a series of point of care diagnostic adjuncts, is promising. Whilst AI is a potentially valuable tool a full evaluation of its current utility and potential is limited by significant variations in research methodology and reporting.
Original languageEnglish
Pages (from-to)133-161
Number of pages29
JournalJournal of Plastic, Reconstructive & Aesthetic Surgery
Early online date23 Nov 2022
Publication statusPublished - Feb 2023

Bibliographical note

Crown Copyright © 2022. Published by Elsevier Ltd. All rights reserved.


  • Artificial intelligence (AI)
  • Machine learning (ML)
  • Burns
  • Diagnostic test meta analyses
  • Systematic review


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