Characteristics of publicly available skin cancer image datasets: a systematic review
Open accessAbstract
Publicly available skin image datasets are increasingly used to develop machine learning algorithms for skin cancer diagnosis. However, the total number of datasets and their respective content is currently unclear. This systematic review aimed to identify and evaluate all publicly available skin image datasets used for skin cancer diagnosis by exploring their characteristics, data access requirements, and associated image metadata. A combined MEDLINE, Google, and Google Dataset search identified 21 open access datasets containing 106 950 skin lesion images, 17 open access atlases, eight regulated access datasets, and three regulated access atlases. Images and accompanying data from open access datasets were evaluated by two independent reviewers. Among the 14 datasets that reported country of origin, most (11 [79%]) originated from Europe, North America, and Oceania exclusively. Most datasets (19 [91%]) contained dermoscopic images or macroscopic photographs only. Clinical information was available regarding age for 81 662 images (76·4%), sex for 82 848 (77·5%), and body site for 79 561 (74·4%). Subject ethnicity data were available for 1415 images (1·3%), and Fitzpatrick skin type data for 2236 (2·1%). There was limited and variable reporting of characteristics and metadata among datasets, with substantial under-representation of darker skin types. This is the first systematic review to characterise publicly available skin image datasets, highlighting limited applicability to real-life clinical settings and restricted population representation, precluding generalisability. Quality standards for characteristics and metadata reporting for skin image datasets are needed.
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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 [11]
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“Prior systematic reviews 9–11 and meta-analyses 12,13 have concentrated on the detection of skin cancer using image-based AI models, with a recent review including 64 studies, of which 52 were focused on detection and 12 concentrated on severity assessment.”
- Authors
- Wen, D., Khan, S. M., Ji Xu, A., Ibrahim, H., Smith, L., Caballero, J., Zepeda, L., de Blas Perez, C., Denniston, A. K., Liu, X., & Matin, R. N.
- Affiliations
- Royal Berkshire Hospital (GB)
- University Hospitals Birmingham NHS Foundation Trust (GB)
- University of Oxford (GB)
- Churchill Hospital (GB)
- Corresponding author
- Matin, Rubeta N
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 4
- Issue
- 1
- Pages
- e64-e74
- ISSN
- 2589-7500 (ISSN-L)
- OpenAlex
- W3212538456
- Cited by
- 243
- References
- 15
- Citations by year
- 2026: 42 · 2025: 59 · 2024: 74 · 2023: 47 · 2022: 20 · 2021: 1
- FWCI
- 4.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, other-oa)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, cc-by)
- Funders
- National Institute for Health Research (AI_HI200014)
- Health Foundation
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- skin cancer, skin lesion images, dermoscopic images, skin cancer diagnosis, image metadata, Fitzpatrick skin type, darker skin types
- MeSH terms
- Machine Learning, Humans, Skin Neoplasms, Dermoscopy, Datasets as Topic
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