Artificial intelligence: its role in dermatopathology
Open accessAbstract
Artificial intelligence (AI), a major frontier in the field of medical research, can potentially lead to a paradigm shift in clinical practice. A type of artificial intelligence system known as convolutional neural network points to the possible utility of deep learning in dermatopathology. Though pathology has been traditionally restricted to microscopes and glass slides, recent advancement in digital pathological imaging has led to a transition making it a potential branch for the implementation of artificial intelligence. The current application of artificial intelligence in dermatopathology is to complement the diagnosis and requires a well-trained dermatopathologist's guidance for better designing and development of deep learning algorithms. Here we review the recent advances of artificial intelligence in dermatopathology, its applications in disease diagnosis and in research, along with its limitations and future potential.
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The Jartarkar review points out how AI models can be used to solve tasks like HER2 expression assessment which are regularly marred by human errors and the need for teamwork and interdisciplinary training.
“The review of Jartarkar (17) further builds on this by pointing out how AI models can be used to solve tasks like HER2 expression assessment which are regularly marred by human errors and the need for teamwork and interdisciplinary training.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [17]
The Introduction says AI can assist with HER2 expression evaluation, a task prone to human error. Its Limitations section calls for interdisciplinary participation to build effective AI algorithms, and the Introduction discusses AI education and training. It does not say that HER2 assessment itself is hampered by a need for teamwork or interdisciplinary training. (LLM, conf=0.83)
Experimental — accuracy not yet measuredIf dermoscopy datasets are employed for training AI models, they can enable diagnostic systems that may be as good as or even better than human diagnosis in certain contexts.
“If the same is employed for training AI models, then these datasets can enable the formation of the basis of diagnostic systems that may be as good or even better than human diagnosis in certain contexts (17).”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [17]
The comparative review outlines technical challenges, such as mislabeling and intra- and inter-class variations, that hinder broader application of AI models.
“The comparative review also outlines technical challenges, such as mislabeling and intra- and inter-class variations, that hinder the broader application of AI models.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [17]
This finding aligns with the results of this scoping review, which showed that CNNs performed well on the chosen datasets.
“This finding aligns with the results of this scoping review, which showed that CNNs performed well on the chosen datasets.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [17]
The Jartarkar review reported rates of 99.3% for dermal nevus, 99.5% for nodular basal cell carcinoma, and 100% for seborrheic keratosis.
“For example, the review by Jartarkar (17), with rates of 99.3% for dermal nevus, 99.5% for nodular basal cell carcinoma, and 100% for seborrheic keratosis.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [17]
The Jartarkar review provides background on the advancement of AI models in dermatopathology, including initial text-based systems such as TEGUMENT, which achieved accuracy 91.8%, to current CNNs, which can perform at par with dermatopathologists.
“For instance, the review of Jartarkar (17) provides a background on the advancement of AI models in dermatopathology, including the initial text-based systems, such as TEGUMENT, which achieved an accuracy rate of 91.8%, to the current CNN, which can perform at par with dermatopathologists.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [17]
Variations in staining intensity, tissue processing, and scanner resolution across different laboratories will affect color and texture features, leading to reduced model reliability and diagnostic errors.
“Variations in staining intensity, tissue processing, and scanner resolution across different laboratories will affect color and texture features, leading to reduced model reliability and diagnostic errors [78].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [78]
The Introduction discusses technical challenges in training dermatopathology AI, but does not link differences among laboratories in staining, tissue processing, or scanner resolution to altered color and texture features or diagnostic errors. It instead reports that one system diagnosed routine images successfully even with poor staining. Searches for the specified variations and their effects found no passage establishing the claimed causal chain. (LLM, conf=0.73)
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Cites
Image analysis and machine learning in digital pathology: challenges and opportunities
Madabhushi, A., & Lee, G. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.06.037 · Cited by 1,093 · 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
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
Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations
Chan, S. Y., Reddy, V. D., Myers, B. A., Thibodeaux, Q. G., Brownstone, N. D., & Liao, W. J. (2020) · Dermatology and Therapy · doi:10.1007/s13555-020-00372-0 · Cited by 275 · 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
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 126 · 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
Artificial 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
Hart, S. N., Flotte, W., Norgan, A. F., Shah, K. K., Buchan, Z. R., Mounajjed, T., & Flotte, T. J. (2019) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_32_18 · Cited by 69 · see citing works
Machine learning applications in the evaluation and management of psoriasis: a systematic review
Yu, K., Syed, M. N., Bernardis, E., & Gelfand, J. M. (2020) · Journal of Psoriasis and Psoriatic Arthritis · doi:10.1177/2475530320950267 · Cited by 65 · 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
Diagnostic Accuracy of Virtual Pathology vs Traditional Microscopy in a Large Dermatopathology Study
Kent, M. N., Olsen, T. G., Feeser, T. A., Tesno, K. C., Moad, J. C., Conroy, M. P., Kendrick, M. J. J., Stephenson, S. R., Murchland, M. R., Khan, A. U., et al. (2017) · JAMA Dermatology · doi:10.1001/jamadermatol.2017.3284 · Cited by 58 · 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
Smart Phone Microscopic Photography: A Novel Tool for Physicians and Trainees
Morrison, A. S., & Gardner, J. M. (2013) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2013-0425-ed · Cited by 49 · 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
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
'Neural network' algorithm to predict severity in epidermolysis bullosa simplex
Eapen, B. R. (2005) · Indian Journal of Dermatology Venereology and Leprology · doi:10.4103/0378-6323.13995 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Colakca, C., Ergın, M., Özensoy, H. S., Şener, A., Gürü, S., & Özhasenekler, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-73229-7 · Cited by 44 · 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
Status and trends of artificial intelligence in the R & D of future fruit & vegetable juice
Niu, H., Zhang, M., Yu, Q. J., & Liu, Y. (2024) · Innovative Food Science & Emerging Technologies · doi:10.1016/j.ifset.2024.103796 · Cited by 19 · see citing works
Artificial intelligence for nonmelanoma skin cancer
Trager, M. H., Gordon, E. R., Breneman, A., Weng, C., & Samie, F. H. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.06.016 · Cited by 11 · see citing works
Han, R., Fan, X., Ren, S., & Niu, X. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1467113 · Cited by 9 · see citing works
Cocuz, I. G., Niculescu, R., Popelea, M. C., Cocuz, M. E., Sabău, A.-H., Tinca, A. C., Cozac-Szőke, A. R., Chiorean, D. M., Budin, C. E., & Cotoi, O. S. (2025) · Diagnostics · doi:10.3390/diagnostics15172196 · Cited by 7 · see citing works
Alhatem, A., Wong, T., & Lambert, W. C. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2023.12.020 · Cited by 5 · see citing works
Applications of Artificial Intelligence in Ophthalmology: Glaucoma, Cornea, and Oculoplastics
Labib, K., Ghumman, H., Jain, S., & Jarstad, J. S. (2024) · Cureus · doi:10.7759/cureus.73522 · Cited by 6 · see citing works
Research advances in evaluation methods for neoadjuvant therapy of tumors
Yuan, Z.-Y., Chen, T., Zhang, H., Li, J.-T., Li, J.-T., & Shang, G.-N. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1580360 · Cited by 3 · see citing works
Deep Learning-Based Evaluation of Ultrasound Images for Benign Skin Tumors
Lee, H.-W., Lee, Y., Jung, S., Lee, S., Oh, B. H., & Yang, S. (2023) · Sensors · doi:10.3390/s23177374 · Cited by 3 · see citing works
Artificial Intelligence and Deep Learning for Skin Image Analysis
Ohaya, C., Ogbaudu, E., Choi, R., & Ko, J. (2025) · Dermatologic Clinics · doi:10.1016/j.det.2025.05.004 · Cited by 2 · see citing works
Holm, S., Zambrana, M., Berner, J. E., Tabrisi, R., Landström, F., Hanoon, D., & ZDOLSEK, J. M. (2025) · Plastic & Reconstructive Surgery Global Open · doi:10.1097/gox.0000000000007057 · Cited by 2 · see citing works
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 · see citing works
“Variations in staining intensity, tissue processing, and scanner resolution across different laboratories will affect color and texture features, leading to reduced model reliability and diagnostic errors [78].”
Revolutionizing dermatopathology using AI in skin diagnostics: scoping review
Rammal, R., Mohy U Din, A., & Alam, T. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1614681 · Cited by 1 · see citing works
“When comparing our results, the scoping review in y Jartarkar (17) reveals various similarities and distinctions.”
InfluentialArtificial intelligence in dermatology: The shifting paradigms
Vasudevan, B., Mitra, D., & Deshwal, V. (2025) · Medical Journal Armed Forces India · doi:10.1016/j.mjafi.2025.08.016 · Cited by 1 · see citing works
Enhancing Melanoma Diagnosis in the USA with AI-Powered Innovations
Paul, D., Hasan, E., Amin, M. A., Mia, T., & Babu, R. M. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-13544-5_20 · Cited by 1 · see citing works
Environmental impact of artificial intelligence in the field of dermatology
Scheinkman, R., Farhadi, L., Kraft, G., Dender, L., Antonevich, S., Sharifi, S., Korsch, M., & Nouri, K. (2026) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2026.05.008
Fan, Y., Zheng, X., Yang, G., Li, P., Ran, Y., Xu, T., & Wei, T. (2026) · Medicine · doi:10.1097/md.0000000000049128
- Authors
- Jartarkar, S. R.
- Corresponding author
- Jartarkar, Shishira R.
- Venue
- Indian Journal of Dermatology Venereology and Leprology
- Publisher
- Scientific Scholar
- Published
- 2022-12-13
- Volume
- 89
- Issue
- 4
- Pages
- 549-552
- ISSN
- 0378-6323 (ISSN-L)
- 0973-3922
- OpenAlex
- W4311364437
- Cited by
- 18
- References
- 25
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 7 · 2023: 1
- FWCI
- 1.07
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-sa
- Locations
- Indian Journal of Dermatology Venereology and Leprology (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- 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, deep learning, convolutional neural networks, digital pathology, computer-aided diagnosis, AI limitations
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Forecasting, Humans, Neural Networks, Computer
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