Shuying Shen
Papers
1
Total Citations
11
H-Index
1
About
Shuying Shen is a forward-thinking researcher at the intersection of medical education and artificial intelligence, with a primary focus on harnessing large language models (LLMs) to transform clinical training. Her most impactful work, a 2025 scoping review titled "Embracing the Future of Medical Education With Large Language Model–Based Virtual Patients," has already garnered 11 citations, signaling its timely relevance. In this seminal paper, Shen systematically analyzes the emerging applications of LLM-driven virtual patients, mapping how these AI systems can create realistic, interactive simulations for medical learners. By synthesizing current research and identifying key opportunities—such as personalized feedback and scalable case-based learning—Shen provides a critical roadmap for integrating conversational AI into curricula. Her contribution is particularly notable for bridging the gap between cutting-edge natural language processing and pedagogical practice, offering educators a framework to adopt these tools responsibly. Shen’s work underscores a pivotal shift: as LLMs evolve, so too must the methods by which future doctors practice diagnostic reasoning and communication. Her research not only highlights the potential of AI to democratize high-fidelity simulation but also raises essential questions about validation and ethics in digital health education.
Research Focus
Key Achievements
Top Papers
- 1