CASE STUDY:
AI Approach Finds Patients up to 15x More At-Risk for Breast Cancer

The challenge of breast health is two fold: many people need diagnostic exams, but at the same time, administering too many unnecessary exams can lead to harm.

How can health systems prioritize high-tough outreach to people most in need while minimizing unnecessary procedures for those who aren't at risk? Identifying risk requires a huge amount of data and nuance, so prioritizing outreach is a challenge best solved by AI. This video and case study shares approaches from Virtua Health:

• Learn how SymphonyRM's Breast Cancer Identification outperformed leading methods such as Gail and Tyrer-Cuzick models
• Hear from Virtua how they implemented and measured the campaign
• Learn how the campaign outperformed previous efforts and the control group by more than 300%