Digital twins in dermatology education: A systematic review and pilot study framework

Summary: Abstract Body: Background: Digital twins—virtual replicas of patients—have emerged as a transformative tool in medical education, offering dynamic, interactive simulations. However, their application in dermatology education remains unexplored. Objective: To evaluate the current use of digital twins in dermatology education and propose a framework for a pilot study to address this gap. Methods: A systematic review was conducted across PubMed, Scopus, Embase, Cochrane and Web of Science to identify studies on digital twins in dermatology education. Despite an exhaustive search, no relevant studies were found. Instead, we reviewed their use in other fields, such as surgery and radiology, to develop a tailored framework for dermatology education. Results: Digital twins have been successfully used in other specialties to simulate rare cases, enhance procedural skills, and provide personalized feedback. Drawing from these insights, we propose a pilot study framework that incorporates AI-powered models representing a diverse range of skin tones, ages, and disease severities; interactive training modules for diagnostics and procedures such as biopsies and cryotherapy, with real-time feedback; and an integrated approach linking virtual microscopy to clinical presentations to deepen understanding of dermatopathology. Conclusion: Although no studies currently explore digital twins in dermatology education, adapting their proven benefits from other fields offers a justified and innovative approach. Our pilot framework aims to evaluate feasibility and educational outcomes, paving the way for integrating digital twins into dermatology training. Impact: This study addresses a critical gap, offering a forward-thinking solution to enhance dermatology education through technology-driven learning. Hossein Akbarialiabad³, Mark M. Melin², Christopher G. Bunick¹ 1. Yale University School of Medicine, New Haven, CT, United States. 2. Mayo Clinic Minnesota, Rochester, MN, United States. 3. University of New South Wales, Sydney, NSW, Australia. Bioinformatics, Computational Biology, and Imaging