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Diagnostics
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Artificial Intelligence in Skin Disorders 2025
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Artificial Intelligence in Skin Disorders 2025

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 6997

Special Issue Editor

Dr. Neil K. Jairath

E-Mail Website
Guest Editor
The Ronald O. Perelman Department of Dermatology, New York University Grossman School of Medicine, New York, NY 10016, USA
Interests: dermatology; AI

Special Issue Information

Dear Colleagues,

The field of dermatology has long been an early adopter of artificial intelligence (AI) technologies. On the heels of rapid advancements in this technology, from computer vision algorithms for image-based diagnostics to large language models assisting in clinical decision making, AI presents novel opportunities to improve the accuracy, efficiency, and personalization of skin disorder management. This Special Issue on “Artificial Intelligence in Skin Disorders 2025” will highlight state-of-the-art research and innovations that leverage AI to address the entire spectrum of dermatological conditions, including inflammatory diseases, autoimmune disorders, complex genetic syndromes, infectious conditions, and various forms of skin cancer.

We particularly encourage submissions that demonstrate novel AI applications beyond traditional image-based diagnostics, including the AI-enabled predictive modeling of treatment responses, personalized risk assessment and stratification, early detection, clinical decision support, and the development of digital assistants and copilots for clinicians. Contributions emphasizing ethical and regulatory considerations, as well as methods for ensuring data privacy and responsible AI deployment, are highly welcome. This Special Issue seeks original research, review articles, and case reports that demonstrate the broad capabilities of AI in facilitating accessible, efficient, and patient-centered dermatological care.

Dr. Neil K. Jairath
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • dermatology
  • computer vision
  • machine learning
  • deep neural networks
  • large language models
  • personalized medicine
  • predictive analytics
  • clinical decision support

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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14 pages, 4655 KB  
Article
Fine-Tuning a Small Vision Language Model Using Synthetic Data for Explaining Bacterial Skin Disease Images
by Shiwan Zhang, Abdurrahim Yilmaz, Gulsum Gencoglan and Burak Temelkuran
Diagnostics 2026, 16(4), 603; https://doi.org/10.3390/diagnostics16040603 - 18 Feb 2026
Viewed by 848
Abstract
Background/Objectives: Vision language models (VLMs) show strong potential for medical image understanding, but their large scale often limits practical deployment. This study investigates whether a compact VLM can be effectively adapted for dermatology, with a focus on explaining bacterial skin disease images. Methods: [...] Read more.
Background/Objectives: Vision language models (VLMs) show strong potential for medical image understanding, but their large scale often limits practical deployment. This study investigates whether a compact VLM can be effectively adapted for dermatology, with a focus on explaining bacterial skin disease images. Methods: We curate a dataset derived from PMC-OA using the BIOMEDICA dataset and construct PMC-derma-VQA-bacteria by pairing images with inherited figure captions and synthetically generated question–answer (QA) supervision produced by Google’s Gemini model. SmolVLM is fine-tuned under three supervision settings: QA-only, caption-only, and a combined QA+caption strategy. The models are evaluated on a held-out test set for both text-generation quality and diagnostic classification performance. Results: QA-only supervision yields the best report-generation performance, while the combined QA+caption setting achieves the highest classification accuracy (70.20%). Conclusions: Synthetic QA supervision can meaningfully enhance compact VLMs for medical image understanding and diagnostic support in dermatology. Full article
(This article belongs to the Special Issue Artificial Intelligence in Skin Disorders 2025)
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Graphical abstract

25 pages, 11175 KB  
Article
An Ingeniously Designed Skin Lesion Classification Model Across Clinical and Dermatoscopic Datasets
by Ying Huang, Zhishuo Zhang, Xin Ran, Kaiwen Zhuang and Yuping Ran
Diagnostics 2025, 15(16), 2011; https://doi.org/10.3390/diagnostics15162011 - 11 Aug 2025
Cited by 1 | Viewed by 1976
Abstract
Background: Skin cancer diagnosis faces critical challenges due to the visual similarity of lesions and dataset limitations. Methods: This study introduces HybridSkinFormer, a robust deep learning model designed to classify skin lesions from both clinical and dermatoscopic images. The model employs [...] Read more.
Background: Skin cancer diagnosis faces critical challenges due to the visual similarity of lesions and dataset limitations. Methods: This study introduces HybridSkinFormer, a robust deep learning model designed to classify skin lesions from both clinical and dermatoscopic images. The model employs a two-stage architecture: a multi-layer ConvNet for local feature extraction and a residual-learnable multi-head attention module for global context fusion. A novel activation function (StarPRelu) and Enhanced Focal Loss (EFLoss) address neuron death and class imbalance, respectively. Results: Evaluated on a hybrid dataset (37,483 images across nine classes), HybridSkinFormer achieved state-of-the-art performance with an overall accuracy of 94.2%, a macro precision of 91.1%, and a macro recall of 91.0%, outperforming nine CNN and ViT baselines. Conclusions: Its ability to handle multi-modality data and mitigate imbalance highlights its clinical utility for early cancer detection in resource-constrained settings. Full article
(This article belongs to the Special Issue Artificial Intelligence in Skin Disorders 2025)
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Review

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17 pages, 286 KB  
Review
Deep Learning Image Processing Models in Dermatopathology
by Apoorva Mehta, Mateen Motavaf, Danyal Raza, Neil Jairath, Akshay Pulavarty, Ziyang Xu, Michael A. Occidental, Alejandro A. Gru and Alexandra Flamm
Diagnostics 2025, 15(19), 2517; https://doi.org/10.3390/diagnostics15192517 - 4 Oct 2025
Cited by 1 | Viewed by 2164
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Artificial Intelligence in Skin Disorders 2025)
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