Daniel Tolks
Papers
1
Total Citations
7
H-Index
1
About
Daniel Tolks is a pioneering researcher in medical education, with a focus on the integration of emerging technologies such as artificial intelligence (AI) and blended learning methodologies. His most-cited work, "Novel Blended Learning on Artificial Intelligence for Medical Students: Qualitative Interview Study" (2025, 7 citations), explores how AI systems—now FDA-approved across multiple specialties—are reshaping clinical practice and medical training. Through qualitative analysis, Tolks examines the implications of AI for future physicians, highlighting the need for curricula that prepare students for AI-assisted decision-making. His contributions lie in bridging the gap between technological innovation and pedagogical practice, offering evidence-based frameworks for implementing blended learning in medical schools. Though early in his citation trajectory, his work is gaining traction among educators and technologists alike, signaling a growing recognition of his role in advancing digital health education. Tolks’ research is particularly notable for its forward-looking approach, addressing how medical students perceive and adapt to AI tools—a critical competency for modern healthcare. His studies serve as a foundational resource for institutions seeking to modernize their training programs in an era of rapid technological change.
Research Focus
Key Achievements
Top Papers
- 1