PH.140.850 - How to AI (for public health)
A course on applying AI tools and methods to public health challenges
First Term 2025
Johns Hopkins Bloomberg School of Public Health
Instructor: Yiqun T. Chen
Office: Wolfe Street Building, E3608
Office Hours: Schedule via email
E-mail: yiqunc@jhu.edu
Course Meeting Times: Mondays 3:30–4:50 pm
Location: Wolfe Street Building, W2015
Anonymous Feedback: https://docs.google.com/forms/u/1/d/12Br8vacTBV0Ix8GEAnjqRt3yny93n15R7HxZLANl9-A/
Course Description
Recent advances in Artificial Intelligence (AI), such as ChatGPT, have shown impressive capabilities in language understanding and generation, demonstrating both fluency and broad knowledge retention. As these tools promise to transform evidence synthesis, idea generation, and the design of innovative solutions, AI is emerging as a pivotal technology for researchers and practitioners across disciplines.
This course introduces students to the practical applications of AI in public health and medicine. Students will use data-driven methods to understand, predict, and address public health challenges. Topics include a high-level overview of modern AI models, with emphasis on their integration into health domains, as well as critical engagement with the ethical, legal, and societal issues related to their deployment. Each class will combine lecture and discussion, including brainstorming sessions on how AI could power and advance students’ research and practice.
Intended Audience: Master’s and PhD students in Biostatistics or other departments with quantitative training, or by instructor permission.
Prerequisites: Familiarity with basic statistics and an interest in biomedical/public health applications are expected. Experience with Python (or R) and coursework in statistical modeling or machine learning are recommended, but not required.
Resources
Course Materials: Readings, lecture slides, and coding notebooks will be posted on this website.
Schedule
| Week | Date | Topic | Materials |
|---|---|---|---|
| 1 | Aug 25 | Brief Review of Modern AI and ML | Slides (PDF) |
| - | Sep 1 | Holiday (Labor Day) - No class | - |
| 2 | Sep 8 | Foundations: Data structure, feature representation, and practical AI tools | Slides (PDF) |
| - | Sep 15 | Class cancelled - Makeup on Oct 3 (Friday) | - |
| 3 | Sep 22 | Continue on word embeddings and intro to large language models | Slides (PDF) |
| 4 | Sep 29 | Large language models | Slides (PDF) |
| 5 | Oct 3 (Fri) | Makeup class: Multimodal Models | TBA |
| 6 | Oct 6 | Agentic AI | TBA |
| 7 | Oct 13 | Evaluation, evaluation, evaluation | TBA |
| 8 | Oct 20 | Discussion of applying AI models to health and medicine | TBA |
Project
Evaluation and Grading: Pass/Fail. Students will need to complete one of the following three project-based assignments for a pass:
- Track A: Data Science Application - Analyze a health-related dataset using AI methods and submit a concise report summarizing your approach and findings.
- Track B: Mathematical Foundations - Complete a set of math and coding exercises that explore the core building blocks of AI techniques (e.g., training simple neural networks).
- Track C: Public Health Focus - Develop an informal research proposal outlining how you plan to apply AI to a public health issue or project of interest.
Learning Objectives
Upon completion of this course, students will be able to:
- Identify major AI methods used in public health
- Apply AI tools to real-world data science problems
- Evaluate ethical, social, and fairness considerations in AI applications to population health