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Artificial Intelligence in Skin Cancer

  • Skin Cancer (E Tongdee and O Markowitz, Section Editors)
  • Published:
Current Dermatology Reports Aims and scope Submit manuscript

Abstract

Purpose

To review recent developments in artificial intelligence for skin cancer diagnosis.

Recent Findings

Major breakthroughs in recent years are likely related to advancements in utilization of convolutional neural networks (CNNs) for dermatologic image analysis, especially dermoscopy. Recent studies have shown that CNN-based approaches perform as well as or even better than human raters in diagnosing close-up and dermoscopic images of skin lesions in a simulated static environment. Several limitations for the development of AI include the need for large data pipelines and ground truth diagnoses, lack of metadata, and lack of rigorous widely accepted standards.

Summary

Despite recent breakthroughs, adoption of AI in clinical settings for dermatology is in early stages. Close collaboration between researchers and clinicians may provide the opportunity to investigate implementation of AI in clinical settings to provide real benefit for both clinicians and patients.

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Authors and Affiliations

  1. Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, 10022, USA

    Ofer Reiter, Veronica Rotemberg, Kivanc Kose & Allan C. Halpern

  2. Sackler Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel

    Ofer Reiter

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Correspondence to Ofer Reiter.

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Conflict of Interest

Allan C. Halpern has done work for Canfield Scientific, Inc. and for the Scibase Advisory Board.

Ofer Reiter, Veronica Rotemberg, and Kivanc Kose declare no conflict of interest.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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Reiter, O., Rotemberg, V., Kose, K. et al. Artificial Intelligence in Skin Cancer. Curr Derm Rep 8, 133–140 (2019). https://doi.org/10.1007/s13671-019-00267-0

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