Artificial Intelligence in Medicine II
BMIF 203
Subject & Catalog Number
Course Information
Description
Researchers and clinicians increasingly use AI methods to study biology, generate hypotheses, and support clinical and healthcare decisions. We organize this course around three paradigms in modern AI, namely foundation models, generative models, and AI agents. For each paradigm, we study the underlying architectures and training strategies and examine how design choices affect model performance. Foundation models learn general representations from large datasets, and researchers can adapt them to many downstream tasks. Generative models learn data distributions and generate new samples such as clinical text, images, and molecules. AI agents combine language models with tools, memory, and planning to perform multistep tasks in research and clinical settings. Students apply these models to clinical, imaging, longitudinal, and molecular data and learn to integrate multiple modalities. Evaluation of AI models is a core part of the course, and we also discuss responsible implementation and the requirements for using these systems in research and clinical practice.
Recommended Prep
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.
Available for Harvard Cross Registration
