Yankai Shi
Papers
1
Total Citations
11
H-Index
1
About
Yankai Shi is a forward-looking researcher at the intersection of artificial intelligence and medical education. Their primary focus lies in harnessing large language models (LLMs) to revolutionize clinical training, with a particular emphasis on developing and evaluating LLM-based virtual patients. Shi’s most cited work, "Embracing the Future of Medical Education With Large Language Model–Based Virtual Patients: Scoping Review" (2025, 11 citations), provides a systematic analysis of how these emerging technologies can create realistic, interactive simulations for learners. This review not only maps the current landscape but also identifies critical gaps and future directions, serving as a foundational resource for educators and technologists alike. By bridging cutting-edge AI with pedagogical needs, Shi is helping to shape a new paradigm where medical students can practice diagnostic reasoning and communication skills in safe, scalable environments. Their contributions are particularly timely as healthcare education seeks to integrate digital tools that enhance, rather than replace, traditional training. With a growing citation impact, Yankai Shi is establishing themselves as a key voice in the responsible adoption of generative AI for competency-based medical education.
Research Focus
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Top Papers
- 1