New Advancements with AI and the Treatment of Melanoma (Skin Cancer)

A recent review of 34 studies (2016-2024) shows that artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), is dramatically improving melanoma diagnosis accuracy from dermoscopy images. These AI-powered tools automate image analysis, reducing subjective interpretations and leading to more precise diagnoses. This is critical because early and accurate melanoma detection significantly improves patient outcomes. ML models like DenseNet and DCNN have already demonstrated over 95% accuracy in identifying this aggressive skin cancer.

While AI integration holds transformative potential for clinical practice, challenges remain. These include ensuring data diversity for robust model performance, improving model interpretability so clinicians understand how AI arrives at a diagnosis, and addressing computational resource needs. Future research should prioritize building larger, more accessible datasets and enhancing model interpretability to facilitate broader clinical adoption. Ultimately, AI-driven diagnostic methods promise to improve diagnostic accuracy and efficiency, becoming essential tools in the fight against melanoma and improving patient care.