AI and Precision Medicine
Learn how AI can transform messy, real-world health data into reliable, personalized treatment recommendations.
Course Information
Certificate Track
About this Course
Discover how artificial intelligence (AI) is helping doctors make better, more personalized treatment decisions. In this course, you'll explore one of the biggest challenges in medicine: how to learn from real-world patient data when that data is messy, incomplete, or biased. You'll be introduced to the ROAD framework, a step-by-step method that cleans up and adjusts real-world health data until it's reliable enough to guide individual treatment decisions, similar to what you'd get from a carefully controlled clinical trial.
By the end, you'll understand how AI is helping bridge the gap between clinical research and everyday medical practice, bringing more precise, evidence-based care to more patients.
AI and Precision Medicine is one of the industry-specific courses in Universal AI, a self-paced program designed to help you go from beginner to AI authority—no coding or technical skills required.
What you'll learn
- Explain why real-world health data is often biased and describe how the ROAD framework corrects for that bias
- Understand how techniques like prognostic matching and weight tuning help make observational data behave more like data from a controlled clinical trial
- Learn how AI identifies which patients are most likely to benefit from a specific treatment, even within a large and varied population
- Explore how the ROAD framework estimates treatment effects using only real-world data, without needing a clinical trial as a reference point
- Compare the three stages of the ROAD framework and understand when and why each approach is used in precision medicine
Meet your instructors
Georgios Antonios Margonis
Associate Professor at Charité - Universitätsmedizin Berlin
Dr. Georgios Antonios Margonis is a senior scientist affiliated with the Department of Surgery at the Memorial Sloan Kettering Cancer Center (MSKCC) and the Operations Research Center of the Massachusetts Institute of Technology (MIT). He earned his MD and PhD degrees from the University of Athens before relocating to the United States in 2014. Since then, he has held positions at Harvard University and the Johns Hopkins University before joining MSKCC.
