@文章{信息:doi/10.2196/22934，作者=“Takiddin, Abdulrahman和Schneider, Jens和Yang, Yin和Abd-Alrazaq, Alaa和Househ, Mowafa”，标题=“人工智能用于皮肤癌检测:范围综述”，期刊=“J医学互联网研究”，年=“2021”，月=“11月”，日=“24”，卷=“23”，数=“11”，页=“e22934”，关键词=“人工智能;皮肤癌;皮肤病变;机器学习;背景:皮肤癌是影响人类最常见的癌症类型。传统的皮肤癌诊断方法成本高昂，需要专业的医生，而且需要时间。因此，为了帮助诊断皮肤癌，人们正在使用人工智能(AI)工具，包括基于浅机器学习和深度机器学习的方法，这些方法经过训练，可以使用计算机算法和深度神经网络来检测和分类皮肤癌。目的:本研究的目的是识别和分组用于检测和分类皮肤癌的不同类型的基于人工智能的技术。该研究还通过研究数据集大小和诊断类数量与用于评估模型的性能指标之间的相关性来检查所选论文的可靠性。方法:我们使用电气与电子工程师学会(IEEE) Xplore、计算机协会数字图书馆(ACM DL)和Ovid MEDLINE数据库，按照系统评价首选报告项和范围评价扩展元分析(PRISMA-ScR)指南对论文进行了系统检索。 The studies included in this scoping review had to fulfill several selection criteria: being specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were independently conducted by two reviewers. Extracted data were narratively synthesized, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. Results: We retrieved 906 papers from the 3 databases, of which 53 were eligible for this review. Shallow AI-based techniques were used in 14 studies, and deep AI-based techniques were used in 39 studies. The studies used up to 11 evaluation metrics to assess the proposed models, where 39 studies used accuracy as the primary evaluation metric. Overall, studies that used smaller data sets reported higher accuracy. Conclusions: This paper examined multiple AI-based skin cancer detection models. However, a direct comparison between methods was hindered by the varied use of different evaluation metrics and image types. Performance scores were affected by factors such as data set size, number of diagnostic classes, and techniques. Hence, the reliability of shallow and deep models with higher accuracy scores was questionable since they were trained and tested on relatively small data sets of a few diagnostic classes. ", issn="1438-8871", doi="10.2196/22934", url="//www.mybigtv.com/2021/11/e22934", url="https://doi.org/10.2196/22934", url="http://www.ncbi.nlm.nih.gov/pubmed/34821566" }