Seeing Into the Future: Machine Learning for Personalized Screening

Artificial Intelligence (AI) has the potential to transform patient care: improving outcomes, reducing costs and eliminating health disparities.  From a computational perspective, these tools must deliver consistent performance across diverse populations while learning from biased and scarce data.  However, the development of equitable clinical AI models and their translation to hospitals remains difficult.  Dr. Yala has demonstrated that these clinical models offer significant improvements over current methods across globally diverse patient populations.

In this talk, Dr. Yala will discuss approaches addressing the above challenges in three areas:

1) Cancer risk assessment from imaging

2) Personalized screening policy design

3) Private data sharing through neural obfuscation