Artificial intelligence in dermatology: a review of methods, clinical applications, and perspectives

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cris.lastimport.scopus2025-12-17T04:11:56Z
dc.abstract.enThe 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.
dc.affiliationWydział Projektowania, Katedra Informatyki
dc.affiliationWydział Projektowania w Warszawie
dc.contributor.authorZbrzezny, Agnieszka
dc.contributor.authorKrzywicki, Tomasz
dc.date.access2025-07-14
dc.date.accessioned2025-08-27T10:40:03Z
dc.date.available2025-08-27T10:40:03Z
dc.date.created2025-07-11
dc.date.issued2025-07-14
dc.description.abstract<jats:p>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.</jats:p>
dc.description.accesstimeat_publication
dc.description.issue14
dc.description.physical1-44
dc.description.versionfinal_published
dc.description.volume15
dc.identifier.doi10.3390/app15147856
dc.identifier.issn2076-3417
dc.identifier.urihttps://share.swps.edu.pl/handle/swps/1707
dc.identifier.weblinkhttps://www.mdpi.com/2076-3417/15/14/7856
dc.languageen
dc.pbn.affiliationinformatyka
dc.rightsCC-BY
dc.rights.questionYes_rights
dc.share.articleOPEN_JOURNAL
dc.subject.endermatology
dc.subject.enartificial intelligence
dc.subject.endeep learning
dc.subject.enclassification
dc.subject.enimage analysis
dc.subject.enAI regulations
dc.swps.sciencecloudsend
dc.titleArtificial intelligence in dermatology: a review of methods, clinical applications, and perspectives
dc.title.journalApplied Sciences
dc.typeJournalArticle
dspace.entity.typeArticle