Department of Dermatology, University of Colorado School of Medicine, Denver, Colorado, USA.
Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Clin Dermatol. 2021 Jul-Aug;39(4):657-666. doi: 10.1016/j.clindermatol.2021.03.011. Epub 2021 Mar 19.
Dermatology and medicine are producing data at an increasing rate that are progressively difficult to sort and manage. Artificial intelligence (AI) and machine learning are examples of tools that may have the capability to produce significant and meaningful results from these data. Currently, AI and machine learning have a variety of applications in medicine including, but not limited to, diagnostics, patient management, preventive medicine, and genomic analysis. Although the role of AI in dermatology is greater than ever, its use is still extremely limited. As AI is continually developed and implemented, it is essential that stakeholders understand AI terminology, applications, limitations, and projected uses in dermatology. With the continued development of AI technology, however, its implementation may afford greater dermatologist efficiency, greater increased patient access to dermatologic care, and improved patient outcomes.
皮肤病学和医学正在以越来越快的速度产生数据,这些数据越来越难以分类和管理。人工智能(AI)和机器学习是可能从这些数据中产生重大而有意义的结果的工具示例。目前,人工智能和机器学习在医学中有多种应用,包括但不限于诊断、患者管理、预防医学和基因组分析。尽管人工智能在皮肤病学中的作用比以往任何时候都更加重要,但它的应用仍然非常有限。随着人工智能的不断发展和实施,利益相关者必须了解人工智能的术语、应用、局限性以及在皮肤病学中的预期用途。然而,随着人工智能技术的不断发展,其实施可能会提高皮肤科医生的效率,增加患者获得皮肤科护理的机会,并改善患者的治疗效果。