Artificial Intelligence in Dermatology: A Review of Methods, Clinical Applications, and Perspectives
The use of artificial intelligence (AI) in dermatology is skyrocketing, but a comprehensive overview integrating regulatory, ethical, validation, and clinical issues is lacking. This work aims to review current research, map applicable legal regulations, identify ethical challenges and methods of ve...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7856 |
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| author | Agnieszka M. Zbrzezny Tomasz Krzywicki |
| author_facet | Agnieszka M. Zbrzezny Tomasz Krzywicki |
| author_sort | Agnieszka M. Zbrzezny |
| collection | DOAJ |
| description | The use of artificial intelligence (AI) in dermatology is skyrocketing, but a comprehensive overview integrating regulatory, ethical, validation, and clinical issues is lacking. This work aims to review current research, map applicable legal regulations, identify ethical challenges and methods of verifying AI models in dermatology, assess publication trends, compare the most popular neural network architectures and datasets, and identify good practices in creating AI-based applications for dermatological use. A systematic literature review is conducted in accordance with the PRISMA guidelines, utilising Google Scholar, PubMed, Scopus, and Web of Science and employing bibliometric analysis. Since 2016, there has been exponential growth in deep learning research in dermatology, revealing gaps in EU and US regulations and significant differences in model performance across different datasets. The decision-making process in clinical dermatology is analysed, focusing on how AI is augmenting skin imaging techniques such as dermatoscopy and histology. Further demonstration is provided regarding how AI is a valuable tool that supports dermatologists by automatically analysing skin images, enabling faster diagnosis and the more accurate identification of skin lesions. These advances enhance the precision and efficiency of dermatological care, showcasing the potential of AI to revolutionise the speed of diagnosis in modern dermatology, sparking excitement and curiosity. Then, we discuss the regulatory framework for AI in medicine, as well as the ethical issues that may arise. Additionally, this article addresses the critical challenge of ensuring the safety and trustworthiness of AI in dermatology, presenting classic examples of safety issues that can arise during its implementation. The review provides recommendations for regulatory harmonisation, the standardisation of validation metrics, and further research on data explainability and representativeness, which can accelerate the safe implementation of AI in dermatological practice. |
| format | Article |
| id | doaj-art-32bfb45ce10f4099bb073d16fccb8dc5 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-32bfb45ce10f4099bb073d16fccb8dc52025-08-20T02:45:33ZengMDPI AGApplied Sciences2076-34172025-07-011514785610.3390/app15147856Artificial Intelligence in Dermatology: A Review of Methods, Clinical Applications, and PerspectivesAgnieszka M. Zbrzezny0Tomasz Krzywicki1Faculty of Mathematics and Computer Science, University of Warmia and Mazury, 10-710 Olsztyn, PolandFaculty of Mathematics and Computer Science, University of Warmia and Mazury, 10-710 Olsztyn, PolandThe use of artificial intelligence (AI) in dermatology is skyrocketing, but a comprehensive overview integrating regulatory, ethical, validation, and clinical issues is lacking. This work aims to review current research, map applicable legal regulations, identify ethical challenges and methods of verifying AI models in dermatology, assess publication trends, compare the most popular neural network architectures and datasets, and identify good practices in creating AI-based applications for dermatological use. A systematic literature review is conducted in accordance with the PRISMA guidelines, utilising Google Scholar, PubMed, Scopus, and Web of Science and employing bibliometric analysis. Since 2016, there has been exponential growth in deep learning research in dermatology, revealing gaps in EU and US regulations and significant differences in model performance across different datasets. The decision-making process in clinical dermatology is analysed, focusing on how AI is augmenting skin imaging techniques such as dermatoscopy and histology. Further demonstration is provided regarding how AI is a valuable tool that supports dermatologists by automatically analysing skin images, enabling faster diagnosis and the more accurate identification of skin lesions. These advances enhance the precision and efficiency of dermatological care, showcasing the potential of AI to revolutionise the speed of diagnosis in modern dermatology, sparking excitement and curiosity. Then, we discuss the regulatory framework for AI in medicine, as well as the ethical issues that may arise. Additionally, this article addresses the critical challenge of ensuring the safety and trustworthiness of AI in dermatology, presenting classic examples of safety issues that can arise during its implementation. The review provides recommendations for regulatory harmonisation, the standardisation of validation metrics, and further research on data explainability and representativeness, which can accelerate the safe implementation of AI in dermatological practice.https://www.mdpi.com/2076-3417/15/14/7856dermatologyartificial intelligencedeep learningclassificationimage analysisAI regulations |
| spellingShingle | Agnieszka M. Zbrzezny Tomasz Krzywicki Artificial Intelligence in Dermatology: A Review of Methods, Clinical Applications, and Perspectives Applied Sciences dermatology artificial intelligence deep learning classification image analysis AI regulations |
| title | Artificial Intelligence in Dermatology: A Review of Methods, Clinical Applications, and Perspectives |
| title_full | Artificial Intelligence in Dermatology: A Review of Methods, Clinical Applications, and Perspectives |
| title_fullStr | Artificial Intelligence in Dermatology: A Review of Methods, Clinical Applications, and Perspectives |
| title_full_unstemmed | Artificial Intelligence in Dermatology: A Review of Methods, Clinical Applications, and Perspectives |
| title_short | Artificial Intelligence in Dermatology: A Review of Methods, Clinical Applications, and Perspectives |
| title_sort | artificial intelligence in dermatology a review of methods clinical applications and perspectives |
| topic | dermatology artificial intelligence deep learning classification image analysis AI regulations |
| url | https://www.mdpi.com/2076-3417/15/14/7856 |
| work_keys_str_mv | AT agnieszkamzbrzezny artificialintelligenceindermatologyareviewofmethodsclinicalapplicationsandperspectives AT tomaszkrzywicki artificialintelligenceindermatologyareviewofmethodsclinicalapplicationsandperspectives |