Explore the applications of artificial intelligence in healthcare through this lecture from MIT's Introduction to Deep Learning course. Delve into topics such as end-to-end lung cancer screening, pathology, genomics, and the potential for higher quality and more equitable learning. Learn about Google's moonshot projects and the challenges of generating labels, addressing bias, and managing uncertainty in AI models. Discover the importance of planning for model limitations and the distinction between healthcare patients and individuals. Gain valuable insights into the future of AI in medicine and participate in a Q&A session to further your understanding of this rapidly evolving field.
Overview
Syllabus
​​ - Introduction
- Applications of AI in healthcare
- End-to-end lung cancer screening
- Pathology
- Genomics
- Higher quality and more equitable learning
- Moonshots at Google
- Generating labels, bias, and uncertainty
- Plan for model limitations
- Healthcare patient vs person
- Summary and conclusion
- Poll results and Q&A
Taught by
https://www.youtube.com/@AAmini/videos
