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Diagnostics
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Artificial Intelligence in Dermatology
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Artificial Intelligence in Dermatology

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: 31 December 2025 | Viewed by 3754

Special Issue Editor

Dr. Choon Chiat Oh

E-Mail Website
Guest Editor
1. Department of Dermatology, Singapore General Hospital, Singapore, Singapore
2. Duke-NUS Medical School, Singapore, Singapore
Interests: skin cancers; skin imaging; innovation devices; oncogenic viruses; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is the latest game changer in medicine, especially in specialties that capture large collections of images, such as dermatology. The continued advances in deep neural networks and novel learning models may provide timely aid to our busy clinicians in various aspects of clinical care and improve patients' outcomes.

We invite researchers, clinicians, and experts in the field to contribute to this Special Issue focusing on the use of artificial intelligence in various subspecialties of dermatology (e.g., dermatopathology, skin cancers, pediatrics dermatology, immunodermatology, skin aging, and skin imaging).  These topics may include the use of AI algorithms in clinical practice, the development of novel AI models, the ethical considerations of AI, data security/ protection in AI, AI cost effectiveness studies, and guidelines and recommendations for AI use in clinical settings.

Notably, this Special Issue invites authors to contribute original articles (AI applied to the diagnosis of specific pathologic conditions), reviews (a summary to clarify the state-of-the-art of a specific area in AI), and technical articles (elements for the adoption of these AI techniques).

We encourage the submission of papers that cover a wide range of topics, including, but not limited to, advances in technologies for AI, as well as novel roles for AI in dermatology. Original research articles, reviews, and case studies are welcome.

Join us in this Special Issue to advance our knowledge and contribute to the development of AI in dermatology. Submissions should adhere to the journal's guidelines and undergo a rigorous peer review process.

Dr. Choon Chiat Oh
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • dermatology
  • artificial intelligence
  • deep learning
  • skin cancers
  • diagnostic imaging

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Published Papers (3 papers)

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20 pages, 3941 KiB  
Article
AΚtransU-Net: Transformer-Equipped U-Net Model for Improved Actinic Keratosis Detection in Clinical Photography
by Panagiotis Derekas, Charalampos Theodoridis, Aristidis Likas, Ioannis Bassukas, Georgios Gaitanis, Athanasia Zampeta, Despina Exadaktylou and Panagiota Spyridonos
Diagnostics 2025, 15(14), 1752; https://doi.org/10.3390/diagnostics15141752 - 10 Jul 2025
Viewed by 332
Abstract
Background: Integrating artificial intelligence into clinical photography offers great potential for monitoring skin conditions such as actinic keratosis (AK) and skin field cancerization. Identifying the extent of AK lesions often requires more than analyzing lesion morphology—it also depends on contextual cues, such as [...] Read more.
Background: Integrating artificial intelligence into clinical photography offers great potential for monitoring skin conditions such as actinic keratosis (AK) and skin field cancerization. Identifying the extent of AK lesions often requires more than analyzing lesion morphology—it also depends on contextual cues, such as surrounding photodamage. This highlights the need for models that can combine fine-grained local features with a comprehensive global view. Methods: To address this challenge, we propose AKTransU-net, a hybrid U-net-based architecture. The model incorporates Transformer blocks to enrich feature representations, which are passed through ConvLSTM modules within the skip connections. This configuration allows the network to maintain semantic coherence and spatial continuity in AK detection. This global awareness is critical when applying the model to whole-image detection via tile-based processing, where continuity across tile boundaries is essential for accurate and reliable lesion segmentation. Results: The effectiveness of AKTransU-net was demonstrated through comparative evaluations with state-of-the-art segmentation models. A proprietary annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis was used to train and evaluate the models. From each photograph, crops of 512 × 512 pixels were extracted using translation lesion boxes that encompassed lesions in different positions and captured different contexts. AKtransU-net exhibited a more robust context awareness and achieved a median Dice score of 65.13%, demonstrating significant progress in whole-image assessments. Conclusions: Transformer-driven context modeling offers a promising approach for robust AK lesion monitoring, supporting its application in real-world clinical settings where accurate, context-aware analysis is crucial for managing skin field cancerization. Full article
(This article belongs to the Special Issue Artificial Intelligence in Dermatology)
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21 pages, 2797 KiB  
Article
Skin Cancer Detection Using Transfer Learning and Deep Attention Mechanisms
by Areej Alotaibi and Duaa AlSaeed
Diagnostics 2025, 15(1), 99; https://doi.org/10.3390/diagnostics15010099 - 3 Jan 2025
Cited by 4 | Viewed by 1980
Abstract
Background/Objectives: Early and accurate diagnosis of skin cancer improves survival rates; however, dermatologists often struggle with lesion detection due to similar pigmentation. Deep learning and transfer learning models have shown promise in diagnosing skin cancers through image processing. Integrating attention mechanisms (AMs) with [...] Read more.
Background/Objectives: Early and accurate diagnosis of skin cancer improves survival rates; however, dermatologists often struggle with lesion detection due to similar pigmentation. Deep learning and transfer learning models have shown promise in diagnosing skin cancers through image processing. Integrating attention mechanisms (AMs) with deep learning has further enhanced the accuracy of medical image classification. While significant progress has been made, further research is needed to improve the detection accuracy. Previous studies have not explored the integration of attention mechanisms with the pre-trained Xception transfer learning model for binary classification of skin cancer. This study aims to investigate the impact of various attention mechanisms on the Xception model’s performance in detecting benign and malignant skin lesions. Methods: We conducted four experiments on the HAM10000 dataset. Three models integrated self-attention (SL), hard attention (HD), and soft attention (SF) mechanisms, while the fourth model used the standard Xception without attention mechanisms. Each mechanism analyzed features from the Xception model uniquely: self-attention examined the input relationships, hard-attention selected elements sparsely, and soft-attention distributed the focus probabilistically. Results: Integrating AMs into the Xception architecture effectively enhanced its performance. The accuracy of the Xception alone was 91.05%. With AMs, the accuracy increased to 94.11% using self-attention, 93.29% with soft attention, and 92.97% with hard attention. Moreover, the proposed models outperformed previous studies in terms of the recall metrics, which are crucial for medical investigations. Conclusions: These findings suggest that AMs can enhance performance in relation to complex medical imaging tasks, potentially supporting earlier diagnosis and improving treatment outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Dermatology)
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21 pages, 779 KiB  
Systematic Review
The Use of Artificial Intelligence for Skin Cancer Detection in Asia—A Systematic Review
by Xue Ling Ang and Choon Chiat Oh
Diagnostics 2025, 15(7), 939; https://doi.org/10.3390/diagnostics15070939 - 7 Apr 2025
Viewed by 1020
Abstract
Background: Artificial intelligence (AI) developed for skin cancer recognition has been shown to have comparable or superior performance to dermatologists. However, it is uncertain if current AI models trained predominantly with lighter Fitzpatrick skin types can be effectively adapted for Asian populations. [...] Read more.
Background: Artificial intelligence (AI) developed for skin cancer recognition has been shown to have comparable or superior performance to dermatologists. However, it is uncertain if current AI models trained predominantly with lighter Fitzpatrick skin types can be effectively adapted for Asian populations. Objectives: A systematic review was performed to summarize the existing use of artificial intelligence for skin cancer detection in Asian populations. Methods: Systematic search was conducted on PubMed and EMBASE for articles published regarding the use of artificial intelligence for skin cancer detection amongst Asian populations. Information regarding study characteristics, AI model characteristics, and outcomes was collected. Conclusions: Current studies show optimistic results in utilizing AI for skin cancer detection in Asia. However, the comparison of image recognition abilities might not be a true representation of the diagnostic abilities of AI versus dermatologists in the real-world setting. To ensure appropriate implementation, maximize the potential of AI, and improve the transferability of AI models across various Asian genotypes and skin cancers, it is crucial to focus on prospective, real-world-based practice, as well as the expansion and diversification of existing Asian databases used for training and validation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Dermatology)
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