With the advancement of artificial intelligence （AI） technology， its potential in medical education is immense.

This study focuses on the possible application of AI in laser therapy training for pigmentation disorders. Findings include：（1）Integrating convolutional neural networks （CNNs） for skin image analysis would enhance trainees' ability to identify lesions；（2） Reinforcement learning combined with patient data to optimize laser parameters would improve the students’ efficacy in personalized treatment plan；（3） Virtual reality （VR）-based simulation platforms significantly reduce hands-on procedural risks. However， challenges persist， such as data annotation quality， human-AI collaboration adaptation， and ethical safety concerns. In that case， this paper proposes to establish multi-center databases， implement a three-phase teaching framework， and develop explainable AI models. The study demonstrates that deep integration of AI technologies will transform pedagogical approaches in laser courses for pigmentation disorders， elevating both educational outcomes and clinical efficacy， thereby advancing intelligent medical education.

Artificial intelligence, Convolutional neural network, Pigmentary disorders, Laser, Medical education