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%
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