Prof Nan Liu

Prof Nan Liu

Director of AI + Medical Sciences Initiative

Position: Director, Duke-NUS AI + Medical Sciences Initiative (DAISI) & Digital Medicine Lab Leader

Specialization: Ethical and Trustworthy AI, Clinical AI Applications, Medical Informatics

Affiliation: Duke-NUS Medical School

Biography

Dr. Nan Liu is Director of the Duke-NUS AI + Medical Sciences Initiative (DAISI) and leads the Digital Medicine Lab at Duke-NUS Medical School. His research focuses on ethical and trustworthy AI, with applications in various clinical fields.

Dr. Liu has received research grants from the Singapore National Medical Research Council and the National Research Foundation. He was recognized as one of the World's Top 2% Scientists by Stanford University and Elsevier.

Key Research Areas

  • Ethical and trustworthy AI in healthcare
  • Clinical AI applications across various medical fields
  • Digital medicine and health informatics
  • AI implementation in clinical practice
  • Medical decision support systems

Leadership Roles

  • Director, Duke-NUS AI + Medical Sciences Initiative (DAISI)
  • Leader, Digital Medicine Lab at Duke-NUS Medical School
  • Principal investigator on multiple research grants

Research Funding

Dr. Liu has successfully secured research grants from prestigious funding bodies, including:

  • Singapore National Medical Research Council
  • National Research Foundation

Honors and Recognition

  • Recognized as one of the World's Top 2% Scientists by Stanford University and Elsevier
  • Principal investigator on multiple research grants
  • Leadership in AI and medical sciences research

Editorial and Professional Roles

Dr. Liu has served as an editor for several prestigious journals, contributing to the advancement of medical AI and digital health research:

  • Editor, npj Digital Medicine
  • Editor, PLOS Medicine

Research Impact

Professor Liu's work focuses on developing ethical and trustworthy AI solutions for healthcare, ensuring that artificial intelligence technologies are implemented responsibly and effectively in clinical settings. His research bridges the gap between cutting-edge AI technology and practical medical applications, with emphasis on patient safety, privacy, and ethical considerations.