Master of Science (AI in Medicine)

Lee Kong Chian School of Medicine at Nanyang Technological University is a forward-looking medical school embedded in a technological university environment. LKCMedicine is committed to integrating high-quality medical training, research and innovation, and translating biomedical discovery into clinical impact. Its culture is one of interdisciplinarity: medicine, engineering, data science, ethics, and health systems all work cooperatively to tackle real-world health challenges. Within LKCMedicine, the Data Science & Artificial Intelligence (DSAI) programme is dedicated to unlocking the potential of biomedical data using advanced analytics and state-of-the art AI methods. DSAI works across a variety of life sciences and medical research areas, focusing on problems such as data integration, high-dimensional data, explainable AI, medical imaging analytics, and other domains where large or complex datasets occur.

Applied Medical AI (Clinician Pathway)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.

Engineering Medical AI (Engineer Pathway)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