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AI and Precision Medicine

via MITx Online

Overview

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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.

Syllabus

  • 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

Taught by

Georgios Antonios Margonis

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