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Automated Dermatological Diagnosis: Hype or Reality?
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“For example, identifying melanoma from images of patients with dark skin is more challenging than those with light skin [70, 71].”
From Fairness of artificial intelligence in healthcare: review and recommendations (2024) · cites this paper as [71]
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Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
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Han, S. S., Kim, M. S., Lim, W., Park, G., Park, I., & Chang, S. E. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.01.028 · Cited by 740
Marchetti, M., Codella, N., Dusza, S., Gutman, D. A., Helba, B., Kalloo, A., Mishra, N. K., Carrera, C., Celebi, M., Defazio, J., et al. (2017) · Journal of American Academy of Dermatology · doi:10.1016/j.jaad.2017.08.016 · Cited by 297
Self-citationMenzies, S. W., Bischof, L., Talbot, H., Gutenev, A., Avramidis, M., Wong, L., Lo, S. K., Mackellar, G., Skladnev, V., McCarthy, W. H., et al. (2005) · Archives of Dermatology · doi:10.1001/archderm.141.11.1388 · Cited by 156
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Ferris, L. K., Harkes, J., Gilbert, B., Winger, D. G., Golubets, K., Akilov, O. E., & Satyanarayanan, M. (2015) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2015.07.028 · Cited by 104
Potential Overdiagnosis of Basal Cell Carcinoma in Older Patients With Limited Life Expectancy
Linos, E., Schroeder, S. A., & Chren, M. (2014) · JAMA · doi:10.1001/jama.2014.9655 · Cited by 49
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Young, A. T., Fernandez, K., Pfau, J., Reddy, R., Cao, N. A., von Franque, M., Johal, A., Wu, B., Wu, R. R., Chen, J. Y., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00380-6 · Cited by 54
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[Translated article] Artificial intelligence in dermatology: A threat or an opportunity?
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Achurra, P., Mery, D., Riquelme, A. J., & Shwaartz, C. G. (2025) · Global Surgical Education - Journal of the Association for Surgical Education · doi:10.1007/s44186-024-00336-4 · Cited by 2
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Tan, J. K. L., Koh, M. J. A., & Navarrete‐Dechent, C. (2025) · JEADV Clinical Practice · doi:10.1002/jvc2.70089 · Cited by 2
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Swetter, S. M., Geller, A. C., Leachman, S. A., Kirkwood, J. M., Katalinic, A., & Gershenwald, J. E. (2018) · Springer eBooks · doi:10.1007/978-3-319-46029-1_6-1 · Cited by 2
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Metadata
- Authors
- Navarrete-Dechent, C., Dusza, S. W., Liopyris, K., Marghoob, A. A., Halpern, A. C., & Marchetti, M. A.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Marchetti, Michael A.
- ORCID
- Venue
- Journal of Investigative Dermatology
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 138
- Issue
- 10
- Pages
- 2277-2279
- ISSN
- 0022-202X (ISSN-L)
- PMC
- PMC7701995
- OpenAlex
- W2806487945
- Cited by
- 129
- References
- 8
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 21 · 2023: 16 · 2022: 21 · 2021: 11 · 2020: 24 · 2019: 10 · 2018: 6
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Investigative Dermatology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Cancer Institute (P30CA008748)
- 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
- artificial intelligence, deep learning, skin cancer classification, dermoscopic images, melanoma, computer-aided diagnosis, clinical implementation barriers
- MeSH terms
- Deep Learning, Algorithms, Humans, Skin Neoplasms
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