Application of Artificial Intelligence in the Diagnostic Interpretation of Skin and Microscopic Images for Early Melanoma Detection: A Literature Review
DOI:
https://doi.org/10.70577/asce.v5i4.1134Keywords:
Artificial intelligence; melanoma; early diagnosis; neural networks; dermatology; deep learningAbstract
Early detection of melanoma represents one of the main challenges in dermatology due to its high aggressiveness and metastatic potential. In this context, artificial intelligence has emerged as a promising tool to support diagnostic interpretation of skin and microscopic images, improving accuracy and efficiency in identifying suspicious lesions. The aim of this study was to analyze the application of artificial intelligence in early melanoma diagnosis through a literature review of recent scientific publications.
The methodology consisted of a documentary review of indexed scientific articles related to machine learning, deep learning, and convolutional neural networks applied to dermatological image analysis. Findings show that artificial intelligence models can achieve diagnostic accuracy levels between 80% and 94%, in several cases surpassing conventional clinical evaluation performance. Additionally, these tools help optimize analysis time, reduce diagnostic errors, and support clinical decision-making.
However, limitations were also identified, including data quality issues, lack of algorithm standardization, and variability in performance across populations. In conclusion, artificial intelligence represents a complementary tool with strong potential to improve early melanoma detection, provided that its clinical implementation is properly validated.
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Copyright (c) 2026 Samantha Gianella Ordoñez Villalba, Xiomara Janeth Ordoñez Villalba, Edmo Ramiro Jara Guerrero
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