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Artificial Intelligence Approach in Melanoma

  • Clara Curiel-Lewandrowski*
  • , Roberto A. Novoa
  • , Elizabeth Berry
  • , M. Emre Celebi
  • , Noel Codella
  • , Felipe Giuste
  • , David Gutman
  • , Allan Halpern
  • , Sancy Leachman
  • , Yuan Liu
  • , Yun Liu
  • , Ofer Reiter
  • , Philipp Tschandl
  • *Corresponding author for this work
  • University of Arizona
  • Stanford University
  • Oregon Health and Science University
  • University of Central Arkansas
  • IBM
  • Emory University
  • Emory University
  • Memorial Sloan-Kettering Cancer Center
  • Alphabet Inc.
  • Medical University of Vienna

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

14 Scopus citations

Abstract

Since its inception in the mid-twentieth century, the field of artificial intelligence (AI) has undergone numerous transformations and retreats. Using large datasets, powerful computers, and modern computational methods, the subset of AI known as machine learning can identify complex patterns in real-world data, yielding observations, associations, and predictions that can match or exceed human capabilities. After decades of promise, the field stands poised to influence a broad range of human endeavors, from the most complex strategic games to autonomous vehicle navigation, financial engineering, and health care. Therefore, the purpose of this chapter is to provide an introduction to AI approaches and medical applications while elaborating on the role of AI in malignant melanoma detection and diagnosis from a healthcare provider and consumer perspective. It is critical that we continue to balance the opportunity and threat of AI in malignant melanoma, as this technology becomes more robust to maximize an effective implementation.

Original languageEnglish
Title of host publicationMelanoma
PublisherSpringer New York
Pages599-628
Number of pages30
ISBN (Electronic)9781461471479
ISBN (Print)9781461471462
DOIs
StatePublished - 1 Jan 2019
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Artificial intelligence
  • Dermatology
  • Dermoscopy
  • Imaging databases
  • Machine learning
  • Medical imaging
  • Melanoma
  • Skin cancer

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