The Emerging Role of Artificial Intelligence in Dermatology: A Systematic Review of Its Clinical Applications
Background: Artificial intelligence (AI) has emerged as a transformative tool in modern medicine, particularly in dermatology, where it supports the diagnosis and management of various skin diseases, including skin cancer. Through machine learning and deep learning techniques, AI enables accurate an...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Dermato |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-6179/5/2/9 |
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| Summary: | Background: Artificial intelligence (AI) has emerged as a transformative tool in modern medicine, particularly in dermatology, where it supports the diagnosis and management of various skin diseases, including skin cancer. Through machine learning and deep learning techniques, AI enables accurate analysis of clinical and dermoscopic images, improving early detection and clinical outcomes. Objective: This systematic review aimed to evaluate the clinical applications of AI in dermatology, focusing on its impact on diagnostic accuracy, workflow efficiency, and access to specialized care. Methods: The review was conducted according to PRISMA guidelines. Peer-reviewed studies published between January 2020 and March 2025 in English or Spanish were included if they evaluated AI-based tools for dermatological diagnosis, classification, or treatment. Animal studies, editorials, non-peer-reviewed articles, and studies with an unclear methodology were excluded. A comprehensive search was performed in PubMed, Scopus, IEEE Xplore, and Google Scholar between December 2024 and March 2025. The risk of bias was assessed qualitatively, using a tailored framework based on study design, dataset transparency, and clinical applicability. Results: A total of 29 studies met the inclusion criteria. AI tools demonstrated high performance in melanoma detection, achieving up to 90% accuracy and 85% sensitivity. In clinical settings, AI support reduced mismanagement of malignant lesions from 58.8% to 4.1% and avoided 27% of unnecessary procedures in benign cases. Additional tools such as convolutional neural networks and imaging systems like FotoFinder also showed promising results. Limitations: Limitations of the evidence include the heterogeneity of AI models, lack of external validation, and a moderate-to-high risk of bias. Conclusions: AI has demonstrated robust clinical potential in dermatology, particularly in cancer detection and workflow optimization. However, further studies are required to address challenges such as algorithmic bias, data privacy, and regulatory oversight. Funding and registration: This review received no external funding and was not registered in a systematic review registry. |
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| ISSN: | 2673-6179 |