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Cited by 134Cutaneous Melanoma Detection and ManagementAI in cancer detectionArtificial Intelligence in Healthcare and Education

Deep learning in dermatology: a systematic review of current approaches, outcomes, and limitations

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Jeong, Hyeon Ki, Park, Christine, Henao, Ricardo, Kheterpal, Meenal·2023·JID Innovations
DOI 10.1016/j.xjidi.2022.100150PMC PMC9841357
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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.

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  • Prior systematic reviews and meta-analyses have concentrated on the detection of skin cancer using image-based AI models.

    “Prior systematic reviews9–11 and meta-analyses12,13 have concentrated on the detection of skin cancer using image-based AI models”

    From Assessing the performance of artificial intelligence models in evaluating inflammatory skin disease severity: a systematic review and meta-analysis (2025) · cites this paper as [10]

    The Introduction describes this as a review of AI applications to dermatological images, and the Results include numerous image-based melanoma and skin-cancer classification studies. But the Abstract and Results also cover other skin conditions, datasets, and clinical applications; the paper is broader than skin-cancer detection alone. No page numbers are available in our copy. (LLM, conf=0.82)

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References 87 · Cited by 134

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Authors
Jeong, H. K., Park, C., Henao, R., & Kheterpal, M.
Affiliations
Duke University (US)
Corresponding author
Kheterpal, Meenal
ORCID
Venue
JID Innovations
Publisher
Elsevier BV
Published
2023-01
Volume
3
Issue
1
Pages
100150
ISSN
2667-0267 (ISSN-L)
DOI
10.1016/j.xjidi.2022.100150
PMC
PMC9841357
OpenAlex
W4292830232
Cited by
134
References
87
Citations by year
2026: 36 · 2025: 51 · 2024: 34 · 2023: 13
FWCI
2.99
Type
journal article
Language
en
Open access
Licence
Locations
  • JID Innovations (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
convolutional neural networks, skin lesion classification, skin cancer prediction, transfer learning, clinical decision support

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