Samuel Edelbring
Papers
3
Total Citations
67
H-Index
2
About
Samuel Edelbring is a leading researcher at the intersection of medical education and artificial intelligence, with a primary focus on clinical reasoning training. His groundbreaking work explores how emerging technologies—particularly social robotics and large language models (LLMs)—can transform virtual patient simulations into more interactive and authentic learning experiences. Edelbring’s most cited studies, including his 2025 mixed-methods investigation (37 citations) and a 2024 qualitative comparison (29 citations), demonstrate that LLM-powered robotic virtual patients significantly enhance medical students’ clinical reasoning skills compared to traditional computer-based simulations. By addressing the critical gap in interactivity and authenticity in virtual patient design, his research directly tackles a pressing patient safety concern—inadequate clinical reasoning among healthcare practitioners. Edelbring’s innovative approach combines rigorous educational methodology with cutting-edge AI, positioning him at the forefront of a paradigm shift in health professions education. His work not only advances pedagogical theory but also offers practical, scalable solutions for training future clinicians, making him a pivotal figure in the ongoing digital transformation of medical training.
Research Focus
Key Achievements
Top Papers
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