Mayo Clinic Alix School of Medicine, Scottsdale, Arizona; Mayo Clinic Office of Artificial Intelligence in Dermatology, Rochester, Minnesota.
Mayo Clinic Office of Artificial Intelligence in Dermatology, Rochester, Minnesota; Department of Dermatology, Mayo Clinic, Rochester, Minnesota; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.
J Am Acad Dermatol. 2022 Dec;87(6):1352-1360. doi: 10.1016/j.jaad.2020.05.053. Epub 2020 May 16.
Because of a convergence of the availability of large data sets, graphics-specific computer hardware, and important theoretical advancements, artificial intelligence has recently contributed to dramatic progress in medicine. One type of artificial intelligence known as deep learning has been particularly impactful for medical image analysis. Deep learning applications have shown promising results in dermatology and other specialties, including radiology, cardiology, and ophthalmology. The modern clinician will benefit from an understanding of the basic features of deep learning to effectively use new applications and to better gauge their utility and limitations. In this second article of a 2-part series, we review the existing and emerging clinical applications of deep learning in dermatology and discuss future opportunities and limitations. Part 1 of this series offered an introduction to the basic concepts of deep learning to facilitate effective communication between clinicians and technical experts.
由于大型数据集的可用性、专门用于图形处理的计算机硬件以及重要理论的进步，人工智能最近为医学领域的重大进展做出了贡献。一种被称为深度学习的人工智能，对医学图像分析特别有影响。深度学习应用在皮肤科和其他专业领域（包括放射科、心脏病学和眼科学）都显示出了很有前景的结果。现代临床医生将受益于对深度学习基本特征的理解，以便有效地使用新的应用程序，并更好地评估其效用和局限性。在这篇由两部分组成的系列文章的第二部分中，我们回顾了深度学习在皮肤科的现有和新兴临床应用，并讨论了未来的机会和局限性。本系列的第一部分介绍了深度学习的基本概念，以促进临床医生和技术专家之间的有效沟通。
