Melanoma is the most aggressive form of skin cancer, early diagnosis and treatment are critical for determining treatment outcomes. Traditional diagnostic methods, such as clinical examination, dermoscopy, and histopathology, provide valuable insights but are limited by subjectivity and interobserver variability. Artificial intelligence (AI), particularly convolutional neural networks, has shown strong potential to enhance melanoma detection, offering accuracy comparable to dermatologists. This review summarizes current and emerging applications of AI in melanoma diagnosis, covering diagnostic tools, educational systems, and smartphone-based apps. Despite encouraging results, challenges such as interpretability, data bias, regulatory hurdles, and ethical issues remain. Integrating AI into clinical workflows, supported by diverse and validated datasets, could significantly improve early therapeutic interventions and patient management.
Authors: Snehankitha Nimmala, Soham Chakraborty, Pooja S., Venkatesh Kamath, Eashan Shetty, Sree Lalitha Bojja, Kiran Kumar Kolathur
DOI: https://dx.doi.org/10.7324/JAPS.2026.257868
Publish Year: 2026
