Background Artificial 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. Methods A 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. Results A total of 46 observational studies were included for review. Assessment of burn depth (n = 26), support vector machines (n = 19) and 10-fold cross-validation (n = 11) were the most common application, algorithm and validation tool used, respectively. Conclusion AI 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.

中文翻译：

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烧伤管理和治疗中的人工智能：系统评价

背景 人工智能 (AI) 是一个具有改善烧伤护理潜力的创新领域。本文提供了关于烧伤护理中机器学习的最新评论，并讨论了未来的挑战以及医疗保健专业人员在成功实施人工智能技术中的作用。方法 在 MEDLINE、Embase 和 PubMed 数据库中系统搜索研究烧伤机器学习的英文文章。文章对临床应用、关键特征、算法、结果和验证方法进行了定量和定性审查。结果共纳入46项观察性研究进行审查。烧伤深度评估 (n = 26)、支持向量机 (n = 19) 和 10 倍交叉验证 (n = 11) 分别是最常见的应用、算法和验证工具。结论 一旦研究了与当前黄金标准的直接比较分析，概述了其益处和风险，人工智能应作为经验丰富的烧伤提供者的辅助手段纳入临床实践。未来的考虑必须包括开发特定于烧录的通用框架。作者应使用通用验证工具进行有效比较。需要 I/II 级证据来提供有关临床和经济影响的有力证据。