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Diagnostics
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AI in Dermatology
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AI 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 July 2025 | Viewed by 1110

Special Issue Editor

Dr. Jonathan Shapiro

E-Mail Website
Guest Editor
Dermatology and Teledermatology Department, Maccabi Healthcare Services, Tel Aviv-Yafo, Israel
Interests: teledermatology; artificial intelligence; machine learning; ChatGPT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is rapidly transforming dermatology, offering new possibilities for diagnosing and managing skin diseases. This Special Issue, "AI in Dermatology", explores AI technologies' latest advancements and applications. AI-driven tools, particularly those utilizing machine learning, enhance how dermatologic data are analyzed. These tools can sift through vast datasets, identifying subtle patterns and anomalies that may be overlooked by human eyes, thereby improving diagnostic accuracy and speed.

Teledermatology is another area where AI is making significant strides. AI algorithms are being integrated into telehealth platforms, enabling remote diagnosis and management of skin conditions with increased reliability. High-resolution imaging and AI-powered analysis provide dermatologists with detailed insights into skin pathology, facilitating early detection and more precise treatment planning.

Natural language processing (NLP) also plays a crucial role in dermatology. NLP algorithms can analyze unstructured medical records and literature, extracting valuable information to support clinical decision-making and research. Wearable AI-enabled devices are also becoming valuable tools, continuously monitoring skin conditions and providing real-time data for proactive management. Furthermore, chatbots and AI systems like ChatGPT are being utilized to offer patients instant support, answer questions, and provide preliminary assessments.

This issue delves into these innovations, highlighting the profound impact of AI, NLP, wearables, and chatbots on dermatologic practice. By showcasing groundbreaking research and applications, we aim to inspire continued exploration and adoption of these technologies to improve patient outcomes in dermatology.

Dr. Jonathan Shapiro
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

  • teledermatology
  • artificial intelligence
  • AI algorithms
  • natural language processing

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Published Papers (1 paper)

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Research

30 pages, 1914 KiB  
Article
Deep Learning Approaches for the Classification of Keloid Images in the Context of Malignant and Benign Skin Disorders
by Olusegun Ekundayo Adebayo, Brice Chatelain, Dumitru Trucu and Raluca Eftimie
Diagnostics 2025, 15(6), 710; https://doi.org/10.3390/diagnostics15060710 - 12 Mar 2025
Viewed by 614
Abstract
Background/Objectives: Misdiagnosing skin disorders leads to the administration of wrong treatments, sometimes with life-impacting consequences. Deep learning algorithms are becoming more and more used for diagnosis. While many skin cancer/lesion image classification studies focus on datasets containing dermatoscopic images and do not include [...] Read more.
Background/Objectives: Misdiagnosing skin disorders leads to the administration of wrong treatments, sometimes with life-impacting consequences. Deep learning algorithms are becoming more and more used for diagnosis. While many skin cancer/lesion image classification studies focus on datasets containing dermatoscopic images and do not include keloid images, in this study, we focus on diagnosing keloid disorders amongst other skin lesions and combine two publicly available datasets containing non-dermatoscopic images: one dataset with keloid images and one with images of other various benign and malignant skin lesions (melanoma, basal cell carcinoma, squamous cell carcinoma, actinic keratosis, seborrheic keratosis, and nevus). Methods: Different Convolution Neural Network (CNN) models are used to classify these disorders as either malignant or benign, to differentiate keloids amongst different benign skin disorders, and furthermore to differentiate keloids among other similar-looking malignant lesions. To this end, we use the transfer learning technique applied to nine different base models: the VGG16, MobileNet, InceptionV3, DenseNet121, EfficientNetB0, Xception, InceptionRNV2, EfficientNetV2L, and NASNetLarge. We explore and compare the results of these models using performance metrics such as accuracy, precision, recall, F1score, and AUC-ROC. Results: We show that the VGG16 model (after fine-tuning) performs the best in classifying keloid images among other benign and malignant skin lesion images, with the following keloid class performance: an accuracy of 0.985, precision of 1.0, recall of 0.857, F1 score of 0.922 and AUC-ROC value of 0.996. VGG16 also has the best overall average performance (over all classes) in terms of the AUC-ROC and the other performance metrics. Using this model, we further attempt to predict the identification of three new non-dermatoscopic anonymised clinical images, classifying them as either malignant, benign, or keloid, and in the process, we identify some issues related to the collection and processing of such images. Finally, we also show that the DenseNet121 model has the best performance when differentiating keloids from other malignant disorders that have similar clinical presentations. Conclusions: The study emphasised the potential use of deep learning algorithms (and their drawbacks), to identify and classify benign skin disorders such as keloids, which are not usually investigated via these approaches (as opposed to cancers), mainly due to lack of available data. Full article
(This article belongs to the Special Issue AI in Dermatology)
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