BIODS 271: Foundation Models for Healthcare
Stanford University, Spring 2025
Course Description
Generative AI and large-scale self-supervised foundation models are poised to have a profound impact on human decision making across occupations. Healthcare is one such area where such models have the capacity to impact patients, clinicians, and other care providers. In this course, we will explore the training, evaluation, and deployment of generative AI and foundation models, with a focus on addressing current and future medical needs. The course will cover models used in natural language processing, computer vision, and multi-modal applications. We will explore the intersection of models trained on non-healthcare domains and their adaptation to domain-specific problems, as well as healthcare-specific foundation models.
Prerequisites
Familiarity with machine learning principles at the level of CS 229, 231N, or 224N
Time & Location
Mon, Wed 3:00 PM - 4:20 PM at Li Ka Shing Center, Room 120
Instructors
TAs
Policies
Coursework will be divided into the following categories:
- Class Participation (10%): Students are encouraged to attend lectures and participate actively by asking questions and offering comments. In-person attendance will be recorded in class via a password-locked Canvas quiz; the password will be announced during lecture. Additional attendance policies are detailed below:
- In-Person Students: In order to receive full credit for class participation, students must obtain 13 attendance points. Attending an in-person lecture and submitting the quiz counts as 1 attendance point. Students who cannot attend in person may watch the recorded lecture and submit the quiz late (by the Sunday following lecture) for 0.5 attendance points.
- SCPD/HCP Students: In order to receive full credit for class participation, students must obtain 13 attendance points. SCPD/HCP students may watch the recorded lecture and submit the quiz by the Sunday following lecture for 1 attendance point.
- Homework Assignments (45%): This course will include three homework assignments, with each homework assignment making up 15% of the course grade. Each student has a total of three late days that can be used throughout the quarter. After the three late days are used up, each additional day late will result in a 25% reduction in the assignment grade.
- Final Project (45%): Students will form teams and choose from one of the suggested projects or select their own project. Teams are expected to work on the research project throughout the second half of the quarter and produce conference-style papers. Each team will present the paper to the entire class at the end of the semester. No late days are permitted for the course project.
We will be using Ed for all discussions.