Fig. 01 -

Title Slide

Fig. 02 -

Disclosures

Fig. 03 -

The MIL

Fig. 04 -

Challenge

Fig. 05 -

Health Inequity Challenges

Fig. 06 -

Health Disparity Paradigm

Fig. 07 -

Differences vs. Disparities

Fig. 08 -

Big Data

Fig. 09 -

Machine Learning 101

Fig. 10 -

System Science

Fig. 11 -

Artificial Intelligence

Fig. 12 -

Augmented Intelligence

Fig. 13 -

Role of AI

Fig. 14 -

Benefits and Risk

Fig. 15 -

Learn like a Baby

Fig. 16 -

Precision Medicine

Fig. 17 -

P3H Framework

Fig. 18 -

Measuring and Diagnostic Tool

Fig. 19 -

AI Bias

Fig. 20 -

Decision Aid

Fig. 21 -

Race Correction

Fig. 22 -

Pain Management

Fig. 23 -

Population Health Management

Fig. 24 -

Race Adjustment

Fig. 25 -

Disparity in Dermatology

Fig. 26 -

More Diverse Data

Fig. 27 -

Finetune Data

Fig. 28 -

Why is AI biased?

Fig. 29 -

How to de-bias AI

Fig. 30 -

Barriers Implementing AI

Fig. 31 -

Future Directions

Fig. 32 -

Making the Exposome

Fig. 33 -

The Vision of Precision

Fig. 34 -

Functional Exposomics

Fig. 35 -

Data, Tools, and Methods

Fig. 36 -

Sensors

Fig. 37 -

Social Determinants

Fig. 38 -

Social Care Navigation

Fig. 39 -

ML/AI Opportunities

Fig. 40 -

Values of Healthcare

Fig. 41 -

Summary