Master of Science in Artificial Intelligence in Medicine 

Lee Kong Chian School of Medicine, Nanyang Technological University offers a stackable postgraduate programme in Artificial Intelligence in Medicine, including a Graduate Certificate, a FlexiMasters pathway, and a full Master of Science (MSc) degree. The programme is designed to integrate cutting-edge AI technologies with medical science, drawing on NTU’s strengths in both medicine and engineering. Through taught modules, project work, and clinical case-studies, students will gain both the theoretical foundations and the practical experience needed to develop, deploy, and evaluate AI tools in healthcare settings.

This programme is ideal for two main groups: (a) healthcare professionals—such as doctors, nurses, public health specialists—who want to deepen their ability to understand, use, and supervise AI in clinical or policy settings; and (b) engineers, data scientists, computer scientists who seek to specialise in healthcare applications of AI, and need grounding in clinical workflow, regulatory & ethical concerns, and collaboration with medicine. Tracks can be tailored to match prior experience: those from clinical backgrounds may focus more on healthcare systems, ethics, and implementation, while those from technical backgrounds may dive deeper into algorithm development, machine learning, and data engineering.

Graduates of the MSc AI in Medicine will be well-positioned for a range of roles at the interface of AI and healthcare. Possible career paths include clinical AI specialist, data scientist in health tech, medical device / diagnostics innovation, healthcare policy & regulation, research roles (e.g., academic, translational or clinical research), or roles in startups and industry deploying AI for patient care, medical imaging, digital health, epidemiology, or precision medicine. Additionally, the programme provides a strong foundation for further study such as PhD work in AI, biomedical informatics, or related interdisciplinary fields.

The MSc in Artificial Intelligence in Medicine is organised into two distinct learning pathways, designed to reflect the different backgrounds and career goals of our students. Both pathways comprise a total of 30 Academic Units (AUs), and each culminates in a capstone project that allows students to integrate their learning and apply it to real-world healthcare challenges.

The Clinician Pathway is tailored for doctors, nurses, and other healthcare professionals who wish to gain a deeper understanding of how AI can be integrated into clinical practice. Students begin with Medical AI Core Courses (11 AUs), which establish a solid grounding in the principles of AI for healthcare. This is complemented by Data Science Foundation Courses (4 AUs), providing essential skills in analytics and computational thinking. Building on this foundation, students progress to Applied Medical AI Courses (9 AUs), which focus on practical applications such as decision support, healthcare data analytics, and AI for population health. The pathway concludes with a Capstone Project (6 AUs), where participants work on an applied problem, often linked to clinical contexts or healthcare systems.

The Engineer Pathway is designed for computer scientists, engineers, and data scientists who want to specialise in healthcare applications of AI. Like the Clinician Pathway, it begins with Medical AI Core Courses (11 AUs), ensuring all students share a common foundation in the fundamentals of AI in medicine. Instead of data science foundations, engineers take Medicine Foundation Courses (4 AUs), which introduce clinical workflows, disease mechanisms, and healthcare system structures—providing the context necessary to design meaningful solutions. Students then advance to Advanced Medical AI Courses (9 AUs), which cover topics such as deep learning for medical imaging, multimodal model integration, and natural language processing for healthcare. The pathway concludes with the Capstone Project (6 AUs), where students tackle research or translational challenges at the interface of AI and medicine.