Department of Health Sciences Research, Division of Digital Health Sciences, Mayo Clinic, Rochester, Minnesota; Mayo Clinic Office of Artificial Intelligence in Dermatology.
Mayo Clinic Office of Artificial Intelligence in Dermatology; Mayo Clinic Alix School of Medicine, Scottsdale, Arizona.
J Am Acad Dermatol. 2022 Dec;87(6):1343-1351. doi: 10.1016/j.jaad.2020.05.056. Epub 2020 May 17.
Artificial intelligence is generating substantial interest in the field of medicine. One form of artificial intelligence, deep learning, has led to rapid advances in automated image analysis. In 2017, an algorithm demonstrated the ability to diagnose certain skin cancers from clinical photographs with the accuracy of an expert dermatologist. Subsequently, deep learning has been applied to a range of dermatology applications. Although experts will never be replaced by artificial intelligence, it will certainly affect the specialty of dermatology. In this first article of a 2-part series, the basic concepts of deep learning will be reviewed with the goal of laying the groundwork for effective communication between clinicians and technical colleagues. In part 2 of the series, the clinical applications of deep learning in dermatology will be reviewed and limitations and opportunities will be considered.
人工智能在医学领域引起了极大的兴趣。人工智能的一种形式，深度学习，已经导致自动图像分析的快速发展。2017 年，一种算法展示了从临床照片中诊断某些皮肤癌的能力，其准确性可与皮肤科专家相媲美。随后，深度学习已应用于一系列皮肤科应用。尽管专家永远不会被人工智能所取代，但它肯定会影响皮肤科这一专业。在这一系列的第一篇文章中，将回顾深度学习的基本概念，目标是为临床医生和技术同事之间的有效沟通奠定基础。在该系列的第 2 部分中，将回顾深度学习在皮肤科的临床应用，并考虑其局限性和机遇。
