AI for Human & Planetary Health
Course Description
How can artificial intelligence (AI) help us prevent chronic diseases and promote long-term health — both for individuals and the planet? This seminar explores the transformative role of AI in understanding, predicting, and mitigating health risks at the intersection of human and environmental well-being.
Students will investigate how AI-powered tools analyze complex health data, detect early warning signs of chronic conditions, and support lifestyle and policy interventions. We will also examine the broader implications of AI in planetary health, including its role in tracking climate-driven health risks, air pollution exposure, and food system sustainability. Through case studies, discussions, and hands-on activities, students will critically assess the promises and challenges of AI-driven approaches to disease prevention.
This course is ideal for students interested in public health, data science, environmental science, and ethical AI. No prior experience in AI or programming is required.
Meet the Instructors:
Titilola Falasinnu
"I’m an Assistant Professor in the Department of Medicine and the Department of Anesthesiology at Stanford University, where I co-direct the Pain Intelligence Lab. My research is centered on applying computational methods — such as machine learning, natural language processing, and real-world data analytics — to advance pain phenotyping, identify meaningful subtypes of pain, and build predictive models for treatment response. Lately, I have been exploring how LLMs can make tasks like patient stratification, symptom documentation, and interpretation of unstructured health data more efficient and scalable, with the goal of making pain-related decision-making more intuitive, evidence-based, and equitable."
Suzanne Tamang
“I'm an Assistant Professor at the Stanford University School of Medicine, Division of Immunology and Rheumatology, and a Faculty Fellow at Stanford’s Center for Population Health Sciences. I also serve as the Computational Systems Evaluation Lead at the Department of Veterans Affairs (VA), where I lead advanced development projects to support national mental health operations. Committed to team science, I work at the intersection of computer science, biomedical informatics, medicine and health services research to drive innovations that improve our scientific understanding of chronic rheumatic and other chronic disease, patient outcomes and inform precision population health strategies.”
Of related interest
