Hyeonhoon Lee
Scholar

Hyeonhoon Lee

Google Scholar ID: Fu3Z-KcAAAAJ
Seoul National University Hospital
Clinical informaticsBiosignal processingReinforcement learningLanguage modelsAgent
Citations & Impact
All-time
Citations
553
 
H-index
12
 
i10-index
14
 
Publications
20
 
Co-authors
11
list available
Resume (English only)
Academic Achievements
  • Early Career Investigator Award (2024) from the Ministry of Health and Welfare, Republic of Korea
  • Top 30 Healthcare R&D Excellence Achievements (2024), Ministry of Health and Welfare
  • Best Poster Award (2024), Korean Society of Medical Informatics (KOSMI)
  • Honorable Mention (2023), Society of Technology in Anesthesia (STA)
  • Grand Prize (Future Talent Award) and First Place (2021), The Association of Korean Medicine
  • Published over 40 peer-reviewed papers in top venues including npj Digital Medicine, JMIR, NeurIPS (oral), and EMNLP
  • Released medical reasoning LLM hari-q3 (14B) in June 2025, achieving 84.1% accuracy on the Korean Medical Licensing Exam (KMLE)
  • Released multilingual medical LLM hari-q2.5 (72B) in May 2025, achieving 84.6% KMLE accuracy
  • Released ClinicalQA dataset (2025) with 1,000+ high-quality clinical question-answer pairs
Background
  • Assistant Professor in the Department of Transdisciplinary Medicine and the Healthcare AI Research Institute at Seoul National University Hospital and Seoul National University College of Medicine
  • Professor in charge of the Center for Data Innovation at the National Strategic Technology Research Institute, leading the Korea Health Data Platform for global collaboration
  • Member of VitalLab, contributing to government-funded projects such as the Korea-US Innovative R&D Initiative, NSTRI, K-MIMIC, Korean ARPA-H, Multimodal Medical AI, and the Sejong Science Fellowship
  • Research focuses on biosignal processing, clinical natural language processing (large language models), reinforcement learning, and multi-agent systems
  • Develops AI models using multimodal clinical data (biosignals, clinical texts, medical imaging, EHRs) for real-time patient monitoring, prognosis prediction, and personalized treatment planning