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Diagnostic performance of multimodal large language models in distinguishing melanoma from nevi in clinical and dermoscopic images
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In melanoma vs. nevus classification (807 images), ChatGPT-4o showed high sensitivity-especially on dermoscopy (96.5%)-but lower specificity, while Gemini 2.0 Flash showed the opposite pattern, with higher specificity; together, they achieved near-complete case capture (96.2%).
“In melanoma vs. nevus classification (807 images), ChatGPT-4o showed high sensitivity—especially on dermoscopy (96.5%)—but lower specificity, while Gemini 2.0 Flash showed the opposite pattern, with higher specificity; together, they achieved near-complete case capture (96.2%) [68].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [68]
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Malignant Melanoma: Skin Cancer-Diagnosis, Prevention, and Treatment
Ahmed, B., Qadir, M. I., & Ghafoor, S. (2020) · Critical Reviews in Eukaryotic Gene Expression · doi:10.1615/critreveukaryotgeneexpr.2020028454 · Cited by 349
Phillips, M. R., Marsden, H., Jaffe, W., Matin, R., Wali, G. N., Greenhalgh, J., McGrath, E., James, R., Ladoyanni, E., Bewley, A., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.13436 · Cited by 236
Precursors to melanoma and their mimics: nevi of special sites
Elder, D. E. (2006) · Modern Pathology · doi:10.1038/modpathol.3800515 · Cited by 190
Can ChatGPT vision diagnose melanoma? An exploratory diagnostic accuracy study
Shifai, N., van Doorn, R., Malvehy, J., & Sangers, T. E. (2024) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2023.12.062 · Cited by 82
Large language models in evaluating hidradenitis suppurativa from clinical images
Boostani, M., Bánvölgyi, A., Zouboulis, C. C., Goldfarb, N. I., Suppa, M., Goldust, M., Lőrincz, K. K., Kiss, T., Nádudvari, N., Holló, P., et al. (2025) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.20861 · Cited by 35
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Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature
Abu-Dayeh, A., Cazzato, G., & Giubellino, A. (2026) · Diagnostics · doi:10.3390/diagnostics16111702 · Cited by 1
“[68].”
Metadata
- Authors
- Boostani, M., Lallas, A., Goldust, M., Nádudvari, N., Lőrincz, K., Bánvölgyi, A., Holló, P., Wikonkál, N. M., Paragh, G., & Kiss, N.
- Affiliations
- Semmelweis University (HU)
- Aristotle University of Thessaloniki (GR)
- Yale University (US)
- Corresponding author
- Kiss, Norbert
- ORCID
- Venue
- JAAD International
- Publisher
- Elsevier BV
- Published
- 2025-12
- Volume
- 23
- Pages
- 58-60
- ISSN
- 2666-3287 (ISSN-L)
- PMC
- PMC12510231
- OpenAlex
- W4414037231
- Cited by
- 21
- References
- 5
- Citations by year
- 2026: 18 · 2025: 3
- FWCI
- 8.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- JAAD International (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Roswell Park Alliance Foundation (2024-2.1.2-EK\u00D6P-KDP-2024-00002, EK\u00D6P-2024-174)
- Semmelweis University
- Nemzeti Kutatási, Fejlesztési és Innovaciós Alap
- 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
- melanoma, nevi, large language models, multimodal models, dermoscopic images, computer-aided diagnosis, skin lesion classification, diagnostic performance
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