Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists
Open accessAbstract
To the Editor: Currently, pathologic melanoma classification is based on the—inevitably somewhat subjective—integration of several histologic features.1 Thus, discordance between pathologists classifying the same lesions can be substantial, and objective assistance tools are needed. The classification of dermoscopic skin lesion images based on convolutional neural networks (CNNs) works well.2 On a histologic level, our pilot studies provided a proof regarding the principle of CNN-based melanoma recognition using tiny sections of hematoxylin-eosin–stained digitized slides.
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Brinker et al.'s ensemble CNN matched or slightly exceeded expert diagnostic performance, achieving AUCs as high as 0.97.
“The ensemble CNN matched or slightly exceeded expert diagnostic performance, achieving AUCs as high as 0.97.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [15]
Brinker et al., 2022, using an ensemble CNN with annotation, achieved 94% mean sensitivity, 90% specificity, and 92% accuracy in classifying melanoma versus benign nevi, while dermatopathologists achieved a mean sensitivity of 88.88%, specificity of 91.77%, and accuracy of 90.33%.
“Brinker et al., 2022 [15], used an ensemble CNN with annotation and achieved 94% mean sensitivity, 90% specificity, and 92% accuracy in classifying melanoma versus benign nevi, while dermatopathologists achieved a mean sensitivity of 88.88%, specificity of 91.77%, and accuracy of 90.33%.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [15]
Brinker et al.'s CNN performance closely aligned with the average sensitivity and specificity of international experts.
“Its performance closely aligned with the average sensitivity and specificity of international experts.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [15]
Well-trained CNNs have potential to assist or support expert-level dermatopathological diagnosis.
“These results highlight the potential for well-trained CNNs to assist or support expert-level dermatopathological diagnosis.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [15]
Deep learning algorithms have shown strong performance in the histopathologic evaluation of melanocytic lesions, often comparable to or exceeding human experts.
“Deep learning algorithms have shown strong performance in the histopathologic evaluation of melanocytic lesions, often comparable to or exceeding human experts”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [15]
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- Authors
- Brinker, T. J., Schmitt, M., Krieghoff-Henning, E. I., Barnhill, R., Beltraminelli, H., Braun, S. A., Carr, R., Fernandez-Figueras, M.-T., Ferrara, G., Fraitag, S., Gianotti, R., Llamas-Velasco, M., Müller, C. S. L., Perasole, A., Requena, L., Sangueza, O. P., Santonja, C., Starz, H., Vale, E., Weyers, W., Hekler, A., Kather, J. N., Fröhling, S., Krahl, D., Holland-Letz, T., Utikal, J. S., Saggini, A., & Kutzner, H.
- Affiliations
- German Cancer Research Center (DE)
- Institut Curie (FR)
- University of Bern (CH)
- University of Münster (DE)
- Warwick Hospital (GB)
- Ospedale di Macerata (IT)
- Hôpital Necker-Enfants Malades (FR)
- University of Milan (IT)
- Ospedale San Bortolo (IT)
- Hospital Universitario Fundación Jiménez Díaz (ES)
- Dermatologie Freiburg (DE)
- Dermatopathologie Friedrichshafen (DE)
- Corresponding author
- Brinker, Titus J.
- ORCID
- Venue
- Journal of the American Academy of Dermatology
- Publisher
- Elsevier BV
- Published
- 2022-03
- Volume
- 86
- Issue
- 3
- Pages
- 640-642
- ISSN
- 0190-9622 (ISSN-L)
- OpenAlex
- W3126261195
- Cited by
- 70
- References
- 5
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 18 · 2023: 13 · 2022: 11 · 2021: 9
- FWCI
- 4.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American Academy of Dermatology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion, other-oa)
- RWTH Publications (RWTH Aachen)
- Funders
- Deutsches Krebsforschungszentrum
- Bundesministerium für Gesundheit
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- melanoma, histopathological diagnosis, convolutional neural networks, artificial intelligence, pathologist agreement, H&E staining, melanoma classification, diagnostic performance
- MeSH terms
- Pathologists, Artificial Intelligence, Humans, Melanoma, Skin Neoplasms
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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