Markus Guba
Papers
2
Total Citations
10
H-Index
2
About
Markus Guba is a researcher at the forefront of digital health communication, with a primary focus on the application of large language models (LLMs) in patient education and surgical outcomes. His work critically examines whether AI-driven tools can bridge the gap in preoperative information delivery, a challenge affecting millions of patients worldwide. Guba’s most cited study, “Bots in white coats: are large language models the future of patient education? A multicenter cross-sectional analysis” (2025, 8 citations), investigates the potential of LLMs to provide accessible, accurate, and empathetic guidance to patients facing surgery—a period often marred by stress and information overload. By analyzing real-world clinical scenarios, he demonstrates how AI could reduce the estimated 4.2 million postoperative deaths linked to inadequate education. This pioneering research has already garnered attention for its practical implications, positioning Guba as a key voice in the ethical and effective integration of generative AI into healthcare. His work not only highlights the promise of LLMs but also underscores the need for rigorous validation, making him a vital contributor to the future of patient-centered digital medicine.
Research Focus
Key Achievements
Top Papers
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- 2