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Applications of Machine Learning, Natural Language Processing, and Generative Artificial Intelligence in Dermatology
Lachlan D W Lau1, Vanessa Tran2, Wendy Chapman3,4
1Department of Dermatology, Western Health, St Albans, Victoria, Australia.
International Journal of Dermatology
|April 15, 2026
Summary
Artificial intelligence (AI) enhances dermatology education and research by offering tools for learning and data analysis. However, challenges like accuracy, bias, and ethical governance require careful management and human oversight for effective implementation.
Area of Science:
- Dermatology
- Medical Education
- Biomedical Research
- Artificial Intelligence
Background:
- Digital health data expansion and advancements in large language models (LLMs) are driving AI adoption in dermatology.
- AI is increasingly utilized in both educational and research settings within dermatology.
Purpose of the Study:
- To conduct a scoping review synthesizing current applications, benefits, and limitations of AI in dermatology education and research.
- To identify key trends and challenges in the integration of AI tools.
Main Methods:
- Systematic scoping review following PRISMA-ScR methodology.
- Inclusion of 102 studies published between 2010 and 2025.
- Categorization of studies into educational (28) and research (74) applications.
Main Results:
- Educational AI applications include LLM-powered exam preparation, content generation, and adaptive imaging tools.
- Research AI applications encompass machine learning, natural language processing for data analysis, pharmacovigilance, text mining, predictive modeling, and LLM-assisted writing.
- Common limitations identified across both domains include issues with accuracy, bias, transparency, and ethical governance.
Conclusions:
- AI holds significant potential to improve dermatology learning experiences and research efficiency.
- Addressing limitations requires human oversight, dermatology-specific datasets, and structured implementation frameworks.
- Future research should evaluate real-world AI performance, reliability, and human-AI collaboration effectiveness in dermatology.
