AI-Driven Diagnostic Approaches for Early Detection and Classification of Skin Cancer: Exploring the Role of Machine Learning and Deep Learning Techniques

Abstract

This research investigates how Artificial Intelligence (AI) enables early diagnosis and taxonomy of skin cancer through its Machine Learning (ML) and Deep Learning (DL) analytic methods. The described technologies improve diagnostic precision and speed up skin cancer identification processes.

Methods: Researchers performed a systematic literature review to assess documents published in PubMed, IEEE Xplore, and Scopus from 2015 to 2025. The review combined findings about AI/ML/DL applications for skin cancer diagnosis using medical images, focusing on dermatological images. This research evaluates data quality alongside algorithm transparency and ethical problems that arose during the study.

Results: The automated classification of skin lesions depends on AI technology, especially Convolutional Neural Networks (CNNs), which demonstrate better accuracy and speed than conventional practices. Analyses performed by AI systems have decreased diagnostic mistakes and enhanced patient results since they enable physicians to act quickly.

Conclusion: The rapid development of sophisticated AI diagnostic tools will result in superior automated skin cancer detection abilities that doctors will reach in future years. AI-based diagnostics will be broadly implemented only after solving the current challenges with algorithm bias and data privacy concerns.