Applications of Machine Learning, Natural Language Processing, and Generative Artificial Intelligence in Dermatology Education and Research: A Scoping Review

Artificial intelligence (AI) is being increasingly used in dermatology education and research as digital health data expands and large language models (LLMs) advance. This scoping review synthesized current applications, benefits, and limitations of AI in these domains. The review followed PRISMA-ScR methodology, including 102 studies published between 2010 and 2025, with 28 studies examining educational applications and 74 examining research applications. Educational applications included the use of LLMs for examination preparation, question and case generation, and image-based learning through generative and adaptive imaging tools. Research applications included machine learning and natural language processing for large-scale data analysis, pharmacovigilance, social media and clinical text mining, predictive modeling, biomarker and gene-signature discovery, and the use of LLMs to support literature synthesis, manuscript writing, and research workflow tasks. Across education and research, key limitations related to accuracy, bias, transparency, and ethical governance. These issues highlight the need for ongoing human oversight, the use of dermatology-specific training datasets, and structured implementation frameworks. Despite these considerations, AI has substantial potential to enhance dermatology learning and improve dermatologic research efficiency. Future work should focus on evaluating real-world performance, model reliability, and the effectiveness of human-AI collaboration in dermatology practice and training.