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Review
. 2023 Jun;25(6):635-645.
doi: 10.1007/s11912-023-01407-3. Epub 2023 Mar 31.

Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods

Sarah Grossarth  1 , Dominique Mosley  2 , Christopher Madden  3   4 , Jacqueline Ike  3   5 , Isabelle Smith  3   6 , Yuankai Huo  7 , Lee Wheless  8   9   10
Affiliations
Review

Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods

Sarah Grossarth et al. Curr Oncol Rep. 2023 Jun.

Abstract

Purpose of review: The purpose was to summarize the current role and state of artificial intelligence and machine learning in the diagnosis and management of melanoma.

Recent findings: Deep learning algorithms can identify melanoma from clinical, dermoscopic, and whole slide pathology images with increasing accuracy. Efforts to provide more granular annotation to datasets and to identify new predictors are ongoing. There have been many incremental advances in both melanoma diagnostics and prognostic tools using artificial intelligence and machine learning. Higher quality input data will further improve these models' capabilities.

Keywords: Artificial intelligence; Deep learning; Dermoscopy; Digital pathology; Machine Learning; Melanoma.

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Conflict of interest statement

Conflicts of Interest The authors declare no conflicts of interest.

Figures

Fig. 1
Fig. 1
Examples of (A), (D) clinical, (B), (E) dermoscopic, and (C), (F) reflectance confocal microscopy images on two individual nevi
See this image and copyright information in PMC

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