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Diagnostics
Volume 15
Issue 19
10.3390/diagnostics15192517
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Review

Deep Learning Image Processing Models in Dermatopathology

by
Apoorva Mehta
1,
Mateen Motavaf
2,
Danyal Raza
2,
Neil Jairath
3,
Akshay Pulavarty
3,
Ziyang Xu
3,
Michael A. Occidental
3,
Alejandro A. Gru
4,* and
Alexandra Flamm
3
1
Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
2
School of Medicine, Duke University, Durham, NC 27710, USA
3
The Ronald O. Perelman Department of Dermatology, New York University Grossman School of Medicine, New York, NY 10016, USA
4
Department of Dermatology Columbia, University Irving Medical Center, 622 W 168th St, New York, NY 10032, USA
*
Author to whom correspondence should be addressed.
Diagnostics 2025, 15(19), 2517; https://doi.org/10.3390/diagnostics15192517 (registering DOI)
Submission received: 8 August 2025 / Revised: 20 September 2025 / Accepted: 2 October 2025 / Published: 4 October 2025
(This article belongs to the Special Issue Artificial Intelligence in Skin Disorders 2025)
Versions Notes

Abstract

Dermatopathology has rapidly advanced due to the implementation of deep learning models and artificial intelligence (AI). From convolutional neural networks (CNNs) to transformer-based foundation models, these systems are now capable of accurate whole-slide analysis and multimodal integration. This review synthesizes the most recent advents of deep-learning architecture and synthesizes its evolution from first-generation CNNs to hybrid CNN-transformer systems to large-scale foundational models such as Paige’s PanDerm AI and Virchow. Herein, we examine performance benchmarks from real-world deployments of major dermatopathology deep learning models (DermAI, PathAssist Derm), as well as emerging next-generation models still under research and development. We assess barriers to clinical workflow adoption such as dataset bias, AI interpretability, and government regulation. Further, we discuss potential future research directions and emphasize the need for diverse, prospectively curated datasets, explainability frameworks for trust in AI, and rigorous compliance to Good Machine-Learning-Practice (GMLP) to achieve safe and scalable deep learning dermatopathology models that can fully integrate into clinical workflows.
Keywords: dermatopathology; deep learning; convolutional neural networks (CNNs); vision transformers (ViTs); foundation models; whole-slide imaging (WSI); dataset bias; good machine learning practice (GMLP) dermatopathology; deep learning; convolutional neural networks (CNNs); vision transformers (ViTs); foundation models; whole-slide imaging (WSI); dataset bias; good machine learning practice (GMLP)

Share and Cite

MDPI and ACS Style

Mehta, A.; Motavaf, M.; Raza, D.; Jairath, N.; Pulavarty, A.; Xu, Z.; Occidental, M.A.; Gru, A.A.; Flamm, A. Deep Learning Image Processing Models in Dermatopathology. Diagnostics 2025, 15, 2517. https://doi.org/10.3390/diagnostics15192517

AMA Style

Mehta A, Motavaf M, Raza D, Jairath N, Pulavarty A, Xu Z, Occidental MA, Gru AA, Flamm A. Deep Learning Image Processing Models in Dermatopathology. Diagnostics. 2025; 15(19):2517. https://doi.org/10.3390/diagnostics15192517

Chicago/Turabian Style

Mehta, Apoorva, Mateen Motavaf, Danyal Raza, Neil Jairath, Akshay Pulavarty, Ziyang Xu, Michael A. Occidental, Alejandro A. Gru, and Alexandra Flamm. 2025. "Deep Learning Image Processing Models in Dermatopathology" Diagnostics 15, no. 19: 2517. https://doi.org/10.3390/diagnostics15192517

APA Style

Mehta, A., Motavaf, M., Raza, D., Jairath, N., Pulavarty, A., Xu, Z., Occidental, M. A., Gru, A. A., & Flamm, A. (2025). Deep Learning Image Processing Models in Dermatopathology. Diagnostics, 15(19), 2517. https://doi.org/10.3390/diagnostics15192517

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