ArticleBurns & trauma2021
Artificial intelligence in the management and treatment of burns: a systematic review.
Article in Burns & trauma, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Data After Death: Post-Mortem Ethics of Burn Patient Records and Images.Journal of burn care & research : official publication of the American Burn Association · 2026Review
- [Current applications and prospects of artificial intelligence in burn diagnosis and treatment].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2026Review
- Enhancing Burn Diagnosis through SE-ResNet18 and Confidence Filtering.Journal of imaging informatics in medicine · 2026Article
- The role of artificial intelligence in burn assessment, complication diagnosis, and outcome prediction: a narrative review.Burns & trauma · 2026Review
- Review
- Comparing Artificial Intelligence Guided Image Assessment to Current Methods of Burn Assessment.Journal of burn care & research : official publication of the American Burn Association · 2025Article
- Deployable machine learning-based decision support system for tracheostomy in acute burn patients.Burns & trauma · 2025Article
- Artificial intelligence in plastic surgery, where do we stand?JPRAS open · 2024Review
- Studies of Artificial Intelligence/Machine Learning Registered on ClinicalTrials.gov: Cross-Sectional Study With Temporal Trends, 2010-2023.Journal of medical Internet research · 2024Article
- Unveiling the Potential of AI in Plastic Surgery Education: A Comparative Study of Leading AI Platforms' Performance on In-training Examinations.Plastic and reconstructive surgery. Global open · 2024Article
- Review of machine learning for optical imaging of burn wound severity assessment.Journal of biomedical optics · 2024Review
- Artificial Intelligence in Plastic Surgery: Insights from Plastic Surgeons, Education Integration, ChatGPT's Survey Predictions, and the Path Forward.Plastic and reconstructive surgery. Global open · 2024Article
- Artificial intelligence and machine learning for hemorrhagic trauma care.Military Medical Research · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is an innovative field with potential for improving burn care. This article provides an updated review on machine learning in burn care and discusses future challenges and the role of healthcare professionals in the successful implementation of AI technologies.
methodsA systematic search was carried out on MEDLINE, Embase and PubMed databases for English-language articles studying machine learning in burns. Articles were reviewed quantitatively and qualitatively for clinical applications, key features, algorithms, outcomes and validation methods.
resultsA total of 46 observational studies were included for review. Assessment of burn depth (
conclusionAI should be incorporated into clinical practice as an adjunct to the experienced burns provider once direct comparative analysis to current gold standards outlining its benefits and risks have been studied. Future considerations must include the development of a burn-specific common framework. Authors should use common validation tools to allow for effective comparisons. Level I/II evidence is required to produce robust proof about clinical and economic impacts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.