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 only open to students in the AIM PhD track and MMSc in Biomedical Informatics programs.
BMIF 203 runs jointly with BMI 702
Assistant Professor of Biomedical Informatics, Harvard Medical School
Associate Faculty, Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University