Artificial intelligence approach in melanoma Book Section


Authors: Curiel-Lewandrowski, C.; Novoa, R. A.; Berry, E.; Celebi, M. E.; Codella, N.; Giuste, F.; Gutman, D.; Halpern, A.; Leachman, S.; Liu, Y.; Liu, Y.; Reiter, O.; Tschandl, P.
Editors: Fisher, D. E.; Bastian, B. C.
Article/Chapter Title: Artificial intelligence approach in melanoma
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. © Springer Science+Business Media, LLC, part of Springer Nature 2019.
Keywords: melanoma; dermoscopy; skin cancer; medical imaging; artificial intelligence; dermatology; machine learning; imaging databases
Book Title: Melanoma
ISBN: 978-1-4614-7148-6
Publisher: Springer  
Publication Place: New York, NY
Date Published: 2019-01-01
Start Page: 599
End Page: 628
Language: English
DOI: 10.1007/978-1-4614-7147-9_43
PROVIDER: scopus
DOI/URL:
Notes: Book chapter: 29 -- This chapter can be found under "Part III Clinical Management" -- Source: Scopus
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  1. Allan C Halpern
    396 Halpern