Integration of artificial intelligence (AI) in dermatological diagnostics and skin analysiss—Contemporary approaches and applications
DOI:
https://doi.org/10.14748/aa1hh205Keywords:
artificial intelligence in dermatology, skin image analysis, computer-aided diagnostics, deep learning in dermatology, digital dermatologyAbstract
Introduction: Artificial intelligence (AI) is rapidly transforming medical practice, particularly in fields reliant on image-based diagnostics such as dermatology. The increasing prevalence of skin diseases and the expanding use of telemedicine highlight the need for accurate and accessible diagnostic tools. As a result, a growing body of research has focused on the application of AI in dermatological diagnosis and skin analysis.
Aim: The aim of the present study is to analyze the scope, developmental directions, achievements, trends, prospects, and challenges in the application of AI in dermatological diagnosis and skin analysis based on contemporary scientific literature.
Materials and Methods: A search of scientific studies was conducted in the electronic databases PubMed, Google Scholar, and ResearchGate using the keywords: artificial intelligence in dermatology, skin image analysis, dermatology imaging, computer-aided diagnosis, deep learning in dermatology, digital dermatopathology. The search period was set from January 2015 to November 2025.
Results: The review includes 62 articles published between 2015 and 2025 related to the integration of AI in dermatological diagnosis and skin analysis. The study shows that the integration of artificial intelligence in dermatology leads to significant improvements in diagnostic accuracy, efficiency, and accessibility, encompassing a wide range of applications—from dermoscopy and clinical visual assessment to dermatopathology, multimodal imaging, and teledermatology systems. The results highlight the need for developing more generalizable, ethically grounded, and interoperable AI models that can be validated across diverse clinical and population contexts, as well as the need for establishing standards, regulatory frameworks, and sustainable infrastructures to ensure the safe, effective, and equitable implementation of AI in dermatological practice.
Conclusion: The integration of AI in dermatological diagnosis and skin analysis has already demonstrated strong potential to enhance diagnostic accuracy, efficiency, and access to care, but requires systematic efforts to overcome methodological, ethical, organizational, and regulatory challenges in order to become a sustainable and equitable component of routine clinical practice.
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