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Personalizing the future of breast cancer screening

48 Hospitals, 22 Countries
Our Mission

 

Breast cancer is the #1 most common cancer in women worldwide. Half of breast cancers develop in women who have no identifiable risk factors other than gender and age.

We believe that every person has the right to know their risk of developing cancer. MIRAI is a deep learning model that produces a personalized risk score up to 5 years in advance just by analyzing a patient’s mammogram, ensuring that cancer can be detected early.

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48 Hospitals
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22 Countries
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2M+ Mammograms validated

MIRAI has been validated extensively on mammograms from patients all over the world. Thanks to the support of Wellcome Trust, we have been able to expand the deployment of MIRAI to include hospitals serving under-resourced regions. This validation process is key to ensuring that MIRAI maintains high performance and that it can be safely used on real patients.

Validation Research

Collaborate with us

Hospitals around the world have installed MIRAI, with some publishing independent research on MIRAI.

We welcome hospitals to join our Hospital Network and try MIRAI.

How does MIRAI work?

In Japanese, “MIRAI” (未来) means future.

MIRAI produces a risk score that helps the clinician determine if a patient requires additional screening if the patient is high-risk, or if the patient can avoid unnecessary screening if they are low-risk.

Evaluate MIRAI’s performance.

The team behind MIRAI

MIT Jameel Clinic AI faculty lead Regina Barzilay decided to build MIRAI after completing her breast cancer treatment in 2016, when she learned there were no clinically-available AI models that assess breast cancer risk.

Joined by then-student Adam Yala and Mass General Brigham radiologists, Barzilay led the team to develop the pioneering technology behind MIRAI.

The scale and computational power needed for bold ideas to work is not possible without the support of grants, foundations, and donors. Your support would enable MIT Jameel Clinic to make a better, healthier future for all.

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The results are quite surprising. In my field, which is health economics, we usually find that when things improve health, they come at a cost to the NHS. Whereas in this case…in the risk-stratified screening approach with Mirai, we find that it actually improves health and reduces costs. Dr. Harry Hill, PhD
University of Sheffield
School of Medicine and Population Health

Core Collaborators

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Adam Yala MIT
Peter Mikhael MIT
Jameel Clinic blue neural network icon
Leslie Lamb Mass General Brigham
Fredrik Strand Karolinska University
Gigin Lin Chang Gung Memorial Hospital
Hari Trivedi Emory University
Kevin Hughes Mass General Hospital
Silvia Sabino Barretos Cancer Hospital
Thiago Silva Barretos Cancer Hospital
Maria Evangelista Barretos Cancer Hospital
Tal Patalon Maccabi Health Services
Michal Guindy Assuta Medical Centers
Regina Barzilay MIT
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