Melanoma is the primary cause of most skin cancer-related fatalities. Timely detection and appropriate treatment considerably boost the survival chances of melanoma patients. Dermoscopy image-based melanoma diagnosis, noted for its precision and ease, is eagerly anticipated. However, despite AI-based diagnostic systems achieving accuracy levels comparable to human dermatologists, their broader application is hindered by concerns regarding privacy, legality, transparency, and interpretability. Addressing these issues to enhance the intelligence and reliability of smart diagnostic systems, particularly in offering interactive features, is imperative. The advent of large language models (LLMs), pre-trained on extensive textual data, presents potential solutions to these challenges. This research aims to establish an AI-assisted diagnosis system for dermoscopy image-based melanoma, combining high recognition accuracy with the interactive capabilities of LLMs. Such a system could aid dermatologists in diagnosis and act as virtual patients for medical training, creating a novel framework for interactive AI-assisted diagnostics using LLMs and marking a pioneering step in applying LLMs to dermoscopy image-based melanoma diagnosis. Aim 1 focuses on developing an AI-driven pipeline to automatically generate textual case descriptions from medical images. Aim 2 involves creating LLMs specialized in melanoma dermoscopy diagnosis. Aim 3 aims to develop and validate AI-driven diagnostic assistants and virtual patients in medical educational settings. This research could significantly impact computer-aided skin cancer diagnosis by bridging the gap between medical image analysis and LLMs. Furthermore, it will pioneer integrating AI-driven virtual patients into medical education, enhancing the training of future medical professionals.