Deep Learning in Dermatology: A Systematic Review of Current Approaches, Outcomes, and Limitations

Hyeon Ki Jeong, Christine Park, Ricardo Henao, Meenal Kheterpal*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

14 Scopus citations

Abstract

Artificial intelligence (AI) has recently made great advances in image classification and malignancy prediction in the field of dermatology. However, understanding the applicability of AI in clinical dermatology practice remains challenging owing to the variability of models, image data, database characteristics, and variable outcome metrics. This systematic review aims to provide a comprehensive overview of dermatology literature using convolutional neural networks. Furthermore, the review summarizes the current landscape of image datasets, transfer learning approaches, challenges, and limitations within current AI literature and current regulatory pathways for approval of models as clinical decision support tools.

Original languageEnglish (US)
Article number100150
JournalJID Innovations
Volume3
Issue number1
DOIs
StatePublished - Jan 2023

ASJC Scopus subject areas

  • Dermatology

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