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Melanoma pp 599-628 | Cite as

Artificial Intelligence Approach in Melanoma

  • Clara Curiel-LewandrowskiEmail author
  • 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
Reference work entry

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.

Keywords

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

Notes

Acknowledgments

We thank Delaney Stratton, RN for her valuable editorial and artistic support.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Clara Curiel-Lewandrowski
    • 1
    Email author
  • Roberto A. Novoa
    • 2
  • Elizabeth Berry
    • 3
  • M. Emre Celebi
    • 4
  • Noel Codella
    • 5
  • Felipe Giuste
    • 6
  • David Gutman
    • 7
  • Allan Halpern
    • 8
  • Sancy Leachman
    • 3
  • Yuan Liu
    • 9
  • Yun Liu
    • 9
  • Ofer Reiter
    • 10
  • Philipp Tschandl
    • 11
  1. 1.Department of DermatologyUniversity of Arizona Cancer Center, University of ArizonaTucsonUSA
  2. 2.Departments of Pathology and DermatologyStanford UniversityStanfordUSA
  3. 3.Department of DermatologyOregon Health and Science UniversityPortlandUSA
  4. 4.Department of Computer ScienceUniversity of Central ArkansasConwayUSA
  5. 5.IBM Research AIArmonkUSA
  6. 6.Department of Biomedical EngineeringEmory University, Georgia TechAtlantaUSA
  7. 7.Department of Neurology & PsychiatryEmory UniversityAtlantaUSA
  8. 8.Department of MedicineMemorial Sloan Kettering Cancer CenterNew YorkUSA
  9. 9.Google AI HealthcareMountain ViewUSA
  10. 10.Dermatology Service, Department of MedicineMemorial Sloan Kettering Cancer CenterNew YorkUSA
  11. 11.ViDIR Group, Department of DermatologyMedical University of ViennaWienAustria

Section editors and affiliations

  • David E. Fisher
    • 1
  • Nick Hayward
    • 2
  • David C. Whiteman
    • 3
  • Keith T. Flaherty
    • 4
  • F. Stephen Hodi
    • 5
    • 6
  • Hensin Tsao
    • 7
    • 8
  • Glenn Merlino
    • 9
  1. 1.Department of Dermatology, Harvard/MGH Cutaneous Biology Research Center, and Melanoma Program, MGH Cancer CenterMassachusetts General Hospital, Harvard Medical SchoolBostonUSA
  2. 2.QIMR Berghofer Medical Research InstituteHerstonAustralia
  3. 3.QIMR Berghofer Medical Research InstituteHerstonAustralia
  4. 4.Henri and Belinda Termeer Center for Targeted TherapiesMGH Cancer CenterCambridgeUSA
  5. 5.FraminghamUSA
  6. 6.Department of Medicine, Brigham and Women’s HospitalDana-Farber Cancer InstituteBostonUSA
  7. 7.AuburndaleUSA
  8. 8.Harvard-MIT Health Sciences and TechnologyCambridgeUSA
  9. 9.Center for Cancer ResearchNational Cancer InstituteBethesdaUSA

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