Breast Cancer Detection Sees Better Results With AI


With AI tech slowly being integrated more into healthcare, we are seeing its benefits with breast cancer detection. One story reported by Cnet.com describes the benefits of AI with a breast cancer diagnosis. In some cases, it was able to detect cancer that was missed by human eyes. Although the current method through a mammogram has saved lives in the past, the AI developed by Google health was able to increase accurate diagnosis.

The Development of AI

Over the past few years, Google’s health division has been working on developing AI to help with inaccurate diagnoses. In a paper published in the journal Nature, the company is teaming up with Deepmind, Cancer Research UK Imperial Centre, Northwestern University and Royal Surrey County Hospital. Artificial intelligence has received backing from not only the United Nations but also the World Health Organization. The AI model has tested scans of over 25,000 women from the UK and over 3,000 women from the US. It then was able to reduce the number of false diagnoses of breast cancer in women.

Statment from Google Health Lead

A statement released by the UK Google Health Lead, Dominic King, King said his team is on its way to developing a tool that can detect breast cancer at a higher accuracy rate. “Further testing, clinical validation, and regulatory approvals are required before this could start making a difference for patients, but we’re committed to working with our partners towards this goal,”.

Breast Cancer Detection In the Future

With continued research, the amount of accurate diagnosis can continue to grow. This is due to the fact that Breast cancer is the most common type of cancer occurring in women globally. This cancer develops in about one in eight women. The current system of detecting it by the use of Mammography still misses one in five breast cancers. With AI and researchers working in tandem, medical professionals can help prevent missed cancer as well as helping healthy women be subject to stress. Since it’s still early in development the goal is to figure out how to best implement both human and AI together.

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