In the United States, the 5-year survival rate is 89%, compared to 70% in the United Kingdom and 64% in China.
In an effort to improve breast cancer survival rates globally, IBM Research is working on a project called ScreenSmart that uses AI to expand access to breast cancer screening. The project is being led by Dr. Francesca Rossi, an IBM Fellow and Chief Scientist for Healthcare and Life Sciences.
Q: What is ScreenSmart and how does it work?
A: ScreenSmart is a project that uses AI to expand access to breast cancer screening. The project is being led by Dr. Francesca Rossi, an IBM Fellow and Chief Scientist for Healthcare and Life Sciences.
The project uses a machine learning algorithm to analyze a woman’s mammogram. The algorithm looks for patterns in the mammogram that are associated with a higher risk of breast cancer. If the algorithm identifies a high-risk pattern, the woman is then referred for further screening.
The goal of the project is to improve breast cancer survival rates by expanding access to screening, particularly in countries where screening rates are low.
Q: Why is AI well suited for this problem?
A: AI is well suited for this problem because it can help to identify patterns in data that would be difficult for humans to discern. The machine learning algorithm that is being used for this project is able to analyze a large number of mammograms and identify patterns that are associated with a higher risk of breast cancer.
Q: How will this project impact breast cancer survival rates?
A: The goal of the project is to improve breast cancer survival rates by expanding access to screening, particularly in countries where screening rates are low. By using AI to identify women at high risk of breast cancer, the project will help to ensure that these women are referred for further screening. This will ultimately lead to more breast cancer cases being detected early, when they are more likely to be treatable.
Q: What are the next steps for the project?
A: The next steps for the project include continuing to refine the machine learning algorithm and expanding the project to additional countries.
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