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

Research output: Chapter in Book/Report/Conference proceedingChapter

13 Scopus citations


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 (US)
Title of host publicationMelanoma
PublisherSpringer New York
Number of pages30
ISBN (Electronic)9781461471479
ISBN (Print)9781461471462
StatePublished - Jan 1 2019
Externally publishedYes


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

ASJC Scopus subject areas

  • General Medicine


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