Google develops AI which can spot advanced cancer more than humans

Google's LYmph Node Assistant or LYNA tool

Google's LYmph Node Assistant or LYNA tool

The scientific report published on this issue explains that: "Artificial Intelligence Algorithms can exhaustively evaluate each occurrence, individually and in detail".

Google has developed an artificial intelligence (AI) programme that is more effective at identifying signs of breast cancer than doctors.

To ideal this Google AI tool, the search giant used a de-identified data set of breast cancer patients' lymph node scans from medical centers in the Netherlands. According to the official blog post by Google, 1 in 4 metastatic lymph nodes stage assessments (which judge how many lymph nodes have been affected by cancer) would be revised in retrospect, and only 38% of small metastases were spotted at all by pathologists, who were given limited time to examine various slides.

LYNA, based on TensorFlow's Inception-v3 image recognition model, passed early tests with flying colors, as described in two separate papers.

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Luckily, Google AI and researchers at the Naval Medical Center San Diego have a solution.

In the right conditions Google AI is able to detect the difference between cancerous and non-cancerous cells in breast tissue 99 percent of the time, particularly when looking for very small metastases than the human eye may not be able to properly see. For comparison, pathologists are on average 81pc accurate at detecting these cells when under time constraints.

"While encouraging, the bench-to-bedside journey to help doctors and patients with these types of technologies is a long one", Mermel and Stumpe admitted.

A member of the search giant's AI team, Yun Liu, told Business Insider that the Lyna "represents a demonstration that people can work really well with AI algorithms than either one alone". "However, we remain optimistic that carefully validated deep learning technologies and well-designed clinical tools can help improve both the accuracy and availability of pathologic diagnosis around the world".

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