Artificial Intelligence in Medicine II
4 credits, Spring Semester
Artificial intelligence (AI) continues to transform medicine, offering cutting-edge approaches to address challenges in medical research and practice. This course covers the foundations of modern AI, including self-supervised learning, generative models, and multimodal techniques with applications to natural language processing, medical image analysis, patients’ medical records, and longitudinal data. The course aims to equip students with both a technical understanding of AI techniques and the implications of these technologies, especially in terms of model and data interpretability, integration into clinical and research workflows, human-AI interaction, and ethical considerations. Materials will be presented through lectures by faculty, readings of contemporary literature, small group research projects, and multiple practical tutorials with hands-on components. Intended primarily for graduate students with good programming skills in Python, knowledge of basic statistics and linear algebra, and practical experience with fundamental data science concepts.
BMIF 203 is a course requirement for students in the AIM PhD track and MMSc in Biomedical Informatics programs. There are limited spots for other students and cross-registration. If you are interested in enrolling, please follow the instructions on this form: https://forms.gle/vCLBJVFasYveWSVVA
BMIF 203 runs jointly with BMI 702
Associate Professor of Biomedical Informatics, Harvard Medical School
Associate Faculty, Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University