Deep learning for dermatologists: Part II. Current applications.
Deep learning for dermatologists: Part II. Current applications.
复制标题
DOI：
10.1016/j.jaad.2020.05.053
复制
发表时间：
2022-12
影响因子：
13.8
通讯作者：
Murphree DH
中科院分区：
文献类型：
--
作者：
Puri P;Comfere N;Drage LA;Shamim H;Bezalel SA;Pittelkow MR;Davis MDP;Wang M;Mangold AR;Tollefson MM;Lehman JS;Meves A;Yiannias JA;Otley CC;Carter RE;Sokumbi O;Hall MR;Bridges AG;Murphree DH
由于大型数据集、图形专用计算机硬件和重要理论进步的可用性的融合，人工智能（AI）最近为医学的巨大进步做出了贡献。一种被称为深度学习（DL）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病学和其他专业领域（包括放射学、心脏病学和眼科学）显示出了良好的效果。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，并更好地衡量其实用性和局限性。在这篇由两部分组成的系列文章中，我们回顾了深度学习在皮肤病学中现有和新兴的临床应用，并讨论了未来的机会和局限性。本系列的第1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。
Due to a convergence of the availability of large datasets, graphics-specific computer hardware, and important theoretical advancements, artificial intelligence (AI) has recently contributed to dramatic progress in medicine. One type of artificial intelligence known as deep learning (DL) 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 in order to effectively use new applications as well as to better gauge their utility and limitations. In this second article of a two 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.
登录
查看更多内容
影响因子：
10.9
作者：
Fine, Jo-David
通讯作者：
Fine, Jo-David
影响因子：
--
作者：
Olsen, Thomas George;Jackson, B Hunter;Soans, Rajath E
通讯作者：
Soans, Rajath E
影响因子：
4.1
作者：
de Zulueta, Paquita C.
通讯作者：
de Zulueta, Paquita C.
影响因子：
10.9
作者：
Lott, Jason P.;Boudreau, Denise M.;Elmore, Joann G.
通讯作者：
Elmore, Joann G.
影响因子：
64.8
作者：
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
通讯作者：
Thrun S