AI Enhancements in Breast Cancer Screening

DomAIn Knowledge

AI Enhancements in Breast Cancer Screening

As National Cancer Prevention Month continues, we take a closer look at how AI contributes to the early detection and prevention of cancer. This week’s Domain Knowledge highlights recent AI developments that are being used to improve breast cancer screening.

Before even getting to the screening itself, AI risk models are being utilized to determine when individuals should be screened for breast cancer. Tempo, a technology for creating risk-based screening guidelines, is being led by scientists from MIT’s Computer Science and Artificial Intelligence Laboratory. Their goal is to create personalized screening for patients to detect breast cancer as early as possible while minimizing costly and stressful false positives.

Mammography is the most prevalent and effective type of breast cancer imaging as of today. AI is being developed and implemented to support breast cancer screening. One new AI model can predict breast cancer risk from mammograms with negative findings. This study shows there is great potential in predicting risk and gaining a better understanding of the contributing factors of breast cancer.

Digital Breast Tomosynthesis (DBT) is an imaging process that uses a series of two-dimensional images to build a three-dimensional image of the breast. A recently developed AI tool is being used to further improve the accuracy of DBT as well as streamline the workflow for radiologists to lower the time-to-diagnosis for patients.

Similarly, AI is also being paired with MRIs to supplement mammogram readings. Research from the Denise Tissue and Early Breast Neoplasm Screening Trial shows AI considerably improves the efficiency and accuracy of readings.

Ultrasound, although not a widely used method for breast cancer screening, is a solid alternative to mammograms as it is widely available, radiation-free, and better at penetrating dense breast tissue. The main drawback has been a higher false-positive rate, but an AI tool created by NYU researchers showed dramatic improvements in accuracy for ultrasound breast exams. There is great potential in pairing AI with ultrasounds to become a primary breast cancer screening option.

That’s a wrap for this week’s review of news and happenings in the healthcare AI space. In closing, I’ll leave you with an invitation to learn more about the benefits of using an AI Hub to manage multiple applications across your clinical needs and offer you a personal demo of Ferrum’s platform and growing AI catalog.

If you have AI tips, suggestions, or resources you’d like to share leave us a note below, and please feel free to suggest topics you would like to see covered in future posts.

Alex Uy

Alex Uy

Alex is a recent UC San Diego graduate with a degree in economics and communications. His focus is digital marketing, and he has a passion for technology driven healthcare solutions.


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