Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer
Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer
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DOI：
10.1016/j.jaad.2020.01.028
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发表时间：
2021-11-18
影响因子：
13.8
通讯作者：
Ho, Roger S.
中科院分区：
文献类型：
--
作者：
Zakhem, George A.;Fakhoury, Joseph W.;Ho, Roger S.
背景：使用人工智能（AI）进行皮肤癌评估已经成为皮肤病学中的一个新兴话题。皮肤科医生的领导是必要的，以确定这些技术如何适应临床实践。目的：描述人工智能在皮肤癌评估中的演变，并描述皮肤科医生参与开发这些技术的特征。方法：通过检索机器学习或人工智能结合皮肤癌或黑色素瘤，在PubMed上进行电子文献检索。如果文章使用由皮肤镜图像或大体病变照片组成的数据集，使用人工智能筛查和诊断皮肤癌，则纳入文章。结果：纳入了51篇文章，其中41%的文章的作者是皮肤科医生。包含皮肤科医生的文章描述了用更多图像构建的算法，而不包含皮肤科医生的文章（平均分别为12,111和660张图像）。在基础技术方面，用于皮肤癌评估的人工智能遵循了图像识别领域的趋势。局限性：本综述关注的是医学文献中描述的模型，没有考虑到其他地方描述的模型。结论：需要皮肤科医生更多地参与思考数据收集、数据集偏差和技术应用中的问题。皮肤科医生可以提供对大型、多样化数据集的访问，这些数据集对建立这些模型越来越重要。[J] .中国生物医学工程学报，2011;22(5):344 - 356。
Background: The use of artificial intelligence (AI) for skin cancer assessment has been an emerging topic in dermatology. Leadership of dermatologists is necessary in defining how these technologies fit into clinical practice. Objective: To characterize the evolution of AI in skin cancer assessment and characterize the involvement of dermatologists in developing these technologies. Methods: An electronic literature search was performed using PubMed by searching machine learning or artificial intelligence combined with skin cancer or melanoma. Articles were included if they used AI for screening and diagnosis of skin cancer using data sets consisting of dermoscopic images or photographs of gross lesions. Results: Fifty-one articles were included, and 41% of these had dermatologists included as authors. Articles that included dermatologists described algorithms built with more images versus articles that did not include dermatologists (mean, 12,111 vs 660 images, respectively). In terms of underlying technology, AI used for skin cancer assessment has followed trends in the field of image recognition. Limitations: This review focused on models described in the medical literature and did not account for those described elsewhere. Conclusions: Greater involvement of dermatologists is needed in thinking through issues in data collection, data set biases, and applications of technology. Dermatologists can provide access to large, diverse data sets that are increasingly important for building these models. ( J Am Acad Dermatol 2021;85:1544-56.)