Artificial intelligence in Dermatopathology
Open accessAbstract
INTRODUCTION: Ever evolving research in medical field has reached an exciting stage with advent of newer technologies. With the introduction of digital microscopy, pathology has transitioned to become more digitally oriented speciality. The potential of artificial intelligence (AI) in dermatopathology is to aid the diagnosis, and it requires dermatopathologists' guidance for efficient functioning of artificial intelligence. METHOD: Comprehensive literature search was performed using electronic online databases "PubMed" and "Google Scholar." Articles published in English language were considered for the review. RESULTS: Convolutional neural network, a type of deep neural network, is considered as an ideal tool in image recognition, processing, classification, and segmentation. Implementation of AI in tumor pathology is involved in the diagnosis, grading, staging, and prognostic prediction as well as in identification of genetic or pathological features. In this review, we attempt to discuss the use of AI in dermatopathology, the attitude of patients and clinicians, its challenges, limitation, and potential opportunities in future implementation.
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The field of inflammatory dermatopathology is largely unexplored by AI systems.
“On the other hand, the field of inflammatory dermatopathology is largely unexplored by AI systems [11,16,17].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [11]
The Conclusion (section 10; page unavailable) describes AI as well trained for melanoma and says further research could extend its use beyond skin cancers to other dermatoses. The Limitations (section 7; page unavailable) also report insufficient image data for various dermatoses. These passages support a broader gap outside skin cancer, but the paper does not specifically assess how much inflammatory dermatopathology AI systems have explored. (LLM, conf=0.78)
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Schadendorf, D., van Akkooi, A. C. J., Berking, C., Griewank, K., Gutzmer, R., Hauschild, A., Stang, A., Roesch, A., & Ugurel, S. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31559-9 · Cited by 1,516 · see citing works
Goyal, M., Knackstedt, T., Yan, S., & Hassanpour, S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104065 · Cited by 461 · see citing works
Machine learning: applications of artificial intelligence to imaging and diagnosis
Nichols, J., Chan, H. H., & Baker, M. A. B. (2018) · Biophysical Reviews · doi:10.1007/s12551-018-0449-9 · Cited by 458 · see citing works
Emerging role of deep learning‐based artificial intelligence in tumor pathology
Jiang, Y., Yang, M., Wang, S., Li, X., & Sun, Y. K. (2020) · 癌症：英文版 · doi:10.1002/cac2.12012 · Cited by 445 · see citing works
Cruz-Roa, A. A., Arevalo Ovalle, J. E., Madabhushi, A., & González Osorio, F. A. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_50 · Cited by 403 · see citing works
Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks
Tschandl, P., Rosendahl, C. O., Akay, B. N., Argenziano, G., Blum, A., Braun, R. P., Cabo, H., Gourhant, J.-Y., Kreusch, J. F., Lallas, A., et al. (2018) · JAMA Dermatology · doi:10.1001/jamadermatol.2018.4378 · Cited by 327 · see citing works
Multimodal skin lesion classification using deep learning
Yap, J., Yolland, W., & Tschandl, P. (2018) · Experimental Dermatology · doi:10.1111/exd.13777 · Cited by 326 · see citing works
Fujisawa, Y., Otomo, Y., Ogata, Y., Nakamura, Y., Fujita, R., Ishitsuka, Y., Watanabe, R., Okiyama, N., Ohara, K., & Fujimoto, M. (2018) · British Journal of Dermatology · doi:10.1111/bjd.16924 · Cited by 316 · see citing works
Brinker, T. J., Hekler, A., Enk, A. H., Klode, J., Hauschild, A., Berking, C., Schilling, B., Haferkamp, S., Schadendorf, D., Fröhling, S., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.02.005 · Cited by 304 · see citing works
Pathologist-level classification of histopathological melanoma images with deep neural networks
Hekler, A., Utikal, J. S., Enk, A. H., Berking, C., Klode, J., Schadendorf, D., Jansen, P., Franklin, C., Holland-Letz, T., Krahl, D., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.04.021 · Cited by 231 · see citing works
Improving Dermoscopy Image Classification Using Color Constancy
Barata, A. F., Celebi, M. E., & Marques, J. S. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2336473 · Cited by 212 · see citing works
Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective
Jutzi, T. B., Krieghoff-Henning, E. I., Holland-Letz, T., Utikal, J. S., Hauschild, A., Schadendorf, D., Sondermann, W., Fröhling, S., Hekler, A., Schmitt, M., et al. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.00233 · Cited by 196 · see citing works
Artificial intelligence applications in dermatology: Where do we stand?
Gomolin, A., Netchiporouk, E., Gniadecki, R., & Litvinov, I. V. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.00100 · Cited by 142 · see citing works
Kulkarni, P. M., Robinson, E. J., Pradhan, J. S., Gartrell, R. D., Rohr, B. R., Trager, M. H., Geskin, L. J., Kluger, H. M., Wong, P. F., Ács, B., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-1495 · Cited by 124 · see citing works
Olsen, T. G., Jackson, B. H., Feeser, T. A., Kent, M. N., Moad, J. C., Krishnamurthy, S., Lunsford, D. D., & Soans, R. E. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_31_18 · Cited by 119 · see citing works
Use of artificial intelligence in dermatology
De, A., Sarda, A., Gupta, S., & DAS, S. K. (2020) · Indian Journal of Dermatology · doi:10.4103/ijd.ijd_418_20 · Cited by 119 · see citing works
Ianni, J. D., Soans, R. E., Sankarapandian, S., Chamarthi, R. V., Ayyagari, D., Olsen, T. G., Bonham, M. J., Stavish, C. C., Motaparthi, K., Cockerell, C. J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-59985-2 · Cited by 113 · see citing works
Self-citationArtificial intelligence in dermatopathology: diagnosis, education, and research
Wells, A., Patel, S., Lee, J. B., & Motaparthi, K. (2021) · Journal of Cutaneous Pathology · doi:10.1111/cup.13954 · Cited by 95 · see citing works
Automated detection of nonmelanoma skin cancer using digital images: a systematic review
Marka, A., Carter, J. B., Toto, E., & Hassanpour, S. (2019) · BMC Medical Imaging · doi:10.1186/s12880-019-0307-7 · Cited by 90 · see citing works
Artificial intelligence in dermatology
Li, C., Shen, C., Xue, K., Shen, X., Jing, Y., Wang, Z.-Y., Xu, F., Meng, R., Yu, J., & Cui, Y. (2019) · Chinese Medical Journal · doi:10.1097/cm9.0000000000000372 · Cited by 75 · see citing works
Logu, F. D., Ugolini, F., Maio, V., Simi, S., Cossu, A., Massi, D., Nassini, R., & Laurino, M. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01559 · Cited by 64 · see citing works
Cho, S. I., Sun, S., Mun, J., Kim, C., Kim, S. Y., Cho, S., Youn, S. W., Kim, H., & Chung, J. H. (2019) · British Journal of Dermatology · doi:10.1111/bjd.18459 · Cited by 59 · see citing works
Schaumberg, A. J., Juarez-Nicanor, W. C., Choudhury, S. J., Guerra, L., Pritt, B. S., Pozuelo, M. P., Sánchez, R. S., Ho, K., Zahra, N., Sener, B. D., et al. (2020) · Modern Pathology · doi:10.1038/s41379-020-0540-1 · Cited by 55 · see citing works
Attitudes Toward Artificial Intelligence Within Dermatopathology: An International Online Survey
Polesie, S., McKee, P. H., Gardner, J. M., Gillstedt, M., Siarov, J., Neittaanmäki, N., & Paoli, J. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.591952 · Cited by 53 · see citing works
Artificial intelligence in dermatology: A practical introduction to a paradigm shift
Eapen, B. R. (2020) · Indian Dermatology Online Journal · doi:10.4103/idoj.idoj_388_20 · Cited by 43 · see citing works
The effect of color constancy algorithms on semantic segmentation of skin lesions
Hewitt, B. M., Yap, M. H., Ng, J., & Goyal, M. (2019) · SPIE Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging · doi:10.1117/12.2512702 · Cited by 29 · see citing works
Computerized dermatopathologic diagnosis
Potter, B., & Ronan, S. G. (1987) · Journal of the American Academy of Dermatology · doi:10.1016/s0190-9622(87)70183-2 · Cited by 25 · see citing works
Xie, P., Zuo, K., Zhang, Y., Li, F., Yin, M., & Lu, K. (2019) · arXiv · doi:10.48550/arxiv.1904.06156 · Cited by 23 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Yu K, Syed MN, Bernardis E, Gelfand JM. Machine learning applica- tion in the evaluation and management of dermatoses: a systematic review. J Ps Ps Arthritis. 2020;5:147-159.
- Schadendorf D, van Akkooi AC, Berking C, et al. Melanoma. Lancet. 2018;392:971-984.
Cited by
Sorted by citations
Skin and Syntax: Large Language Models in Dermatopathology
Shah, A., Wahood, S., Guermazi, D., Brem, C. E., & Saliba, E. (2024) · Dermatopathology · doi:10.3390/dermatopathology11010009 · Cited by 25 · see citing works
Artificial intelligence in dermatopathology: Updates, strengths, and challenges
Cazzato, G., & Rongioletti, F. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.06.010 · Cited by 24 · see citing works
McCrary, M. R., Galambus, J., & Chen, W. (2024) · Journal of Cutaneous Pathology · doi:10.1111/cup.14631 · Cited by 12 · see citing works
Della Mura, M., Sorino, J., Colagrande, A., Daruish, M., Ingravallo, G., Massaro, A., Cazzato, G., Lupo, C., Casatta, N., Ribatti, D., et al. (2025) · Medical Sciences · doi:10.3390/medsci13020070 · Cited by 6 · see citing works
“Finally, the fully connected layers integrate all processed features to generate the final output, such as image classification or object localization (e.g., type or site of the lesion) [6,8,11,12].”
Influential
- Authors
- Jartarkar, S. R., Cockerell, C. J., Patil, A., Kassir, M., Babaei, M., Weidenthaler‐Barth, B., Grabbe, S., & Goldust, M.
- Affiliations
- Rajiv Gandhi University of Health Sciences (IN)
- The University of Texas Southwestern Medical Center (US)
- DY Patil Deemed to be University (IN)
- Worldwide Laser Institute (US)
- Stanford Medicine (US)
- University Medical Center of the Johannes Gutenberg University Mainz (DE)
- Corresponding author
- Goldust, Mohamad
- ORCID
- Venue
- Journal of Cosmetic Dermatology
- Publisher
- Wiley
- Published
- 2022-12-22
- Volume
- 22
- Issue
- 4
- Pages
- 1163-1167
- ISSN
- 1473-2130 (ISSN-L)
- 1473-2165
- OpenAlex
- W4312095724
- Cited by
- 17
- References
- 28
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 8
- FWCI
- 1.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Cosmetic Dermatology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Gutenberg Open Science (publishedVersion, cc-by)
- Gutenberg Open Science (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Keywords
- artificial intelligence, dermatopathology, convolutional neural networks, digital pathology, skin tumor diagnosis, tumor staging and grading, outcome prediction, image segmentation
- MeSH terms
- Artificial Intelligence, Dermatology, Humans
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