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.

中文翻译：

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描述皮肤科医生在开发用于评估皮肤癌的人工智能中的作用

背景

使用人工智能 (AI) 进行皮肤癌评估一直是皮肤病学的一个新兴话题。皮肤科医生的领导对于定义这些技术如何融入临床实践是必要的。

客观的

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情况。

方法

使用 PubMed 通过搜索机器学习或人工智能结合皮肤癌或黑色素瘤进行电子文献搜索。如果他们使用由皮肤镜图像或大体病变照片组成的数据集使用 AI 来筛查和诊断皮肤癌，则文章将被包括在内。

结果

纳入了 51 篇文章，其中 41% 的作者包括皮肤科医生。与不包括皮肤科医生的文章相比，包含皮肤科医生的文章描述了使用更多图像构建的算法（平均分别为 12,111 和 660 张图像）。在底层技术方面，用于皮肤癌评估的人工智能已经跟随图像识别领域的趋势。

限制

本综述侧重于医学文献中描述的模型，并未考虑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方面的问题。皮肤科医生可以提供对构建这些模型越来越重要的大型、多样化数据集的访问。