AI-Powered Dermatology: Revolutionizing Skin Cancer Detection Through Machine Learning Algorithms

Skin cancer is among the most prevalent cancers and is usually diagnosed at the initial stages to increase its survival rate; therefore, this research develops an RL model to improve AI diagnostic systems for skin cancer. These RL models incorporate expert reward functions, enhancing diagnostic performance, especially in melanomas and BCC, with an overall classification accuracy of 82%., outperforming traditional CFT models by 78%. In particular, the sensitivity of melanoma rose from 61. 5%, and the BCC detection sensitivity increased from 79. 1%, a remarkable performance improvement, which shows the reduction in errors on both accounts. This pilot study proved that if more recent investigations tried to increase clinics' practical value using this potential, the inquiry would look feasible to enhance accuracy and decision-making power at the same time.