在烧伤评估中使用人工智能:使用大型语言模型生成的决策树进行范围审查
Sebastian Holm1, Fredrik Huss2,3, Bahaman Nayyer4
1Department of Plastic and Reconstructive Surgery, Örebro University Hospital, Faculty of Medicine and Health, Örebro University, 70182 Örebro, Sweden.
European burn journal
|January 21, 2026
概括
卷积神经网络 (CNNs) 在评估烧伤总体表面积 (TBSA) 和深度方面表现有前途. 然而,不同的研究设计和有限的验证阻碍了临床使用,尽管AI在烧伤护理中的潜力.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 烧伤手术 烧伤手术
背景情况:
- 烧伤导致全球显著的死亡率和发病率,特别是在LMICs.
- 目前的烧伤深度和TBSA评估是主观的,依赖于视觉和床边检查.
- 卷积神经网络 (CNN) 为客观,基于图像的烧伤评估提供了潜力,但临床适用性尚未被证明.
研究的目的:
- 审查CNN在燃烧TBSA,深度和治疗任务方面的表现的证据.
- 评估使用大型语言模型 (LLM) 来从提取的发现中创建决策树的可行性.
主要方法:
- 根据PRISMA-ScR指南进行范围审查.
- 搜索了PubMed,科学网和科克兰数据库.
- 包括24项研究分析2D燃烧图像与CNN和报告性能指标.
主要成果:
- 据报道,CNN对TBSA和深度评估的表现往往很高,但非常可变.
- 研究设计,数据集,成像和验证方法存在显著的异质性.
- 一项单一的研究显示了对移植预测的高准确性,但结果不能通用.
结论:
- CNNs展示了燃烧TBSA和深度评估的潜力.
- 异质性,数据限制和外部验证不足阻碍了临床翻译.
- 由LLM生成的决策树是作为文献综合工具,而不是临床决策支持.
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