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AI system outperforms radiologists at predicting breast cancer

Health Industry Hub | January 7, 2020 |

An artificial intelligence (AI) system outperformed radiologists at predicting breast cancer, according to a study led by Google Health. The team behind the technology hopes it can be widely deployed to improve cancer care.

Despite the existence of screening programs worldwide, interpretation of
screening mammography suffers from sub-optimal rates of false positives and false negatives.

The authors presented an AI system capable of surpassing a single expert reader in breast cancer prediction performance. Using two large data sets representative of clinical practice in the United States (US) and the United Kingdom (UK), the study showed an absolute reduction of 5.7% / 1.2% (US/UK) in false positives and 9.4% / 2.7% (US/UK) in false negatives, as confirmed by biopsy.

In an independently-conducted reader study, the AI system out-performed all six radiologists with an area under the receiver operating characteristic curve (AUC-ROC) greater than the average radiologist by an absolute margin of 12.1%. By simulating the AI system’s role in the double-reading process, the authors maintain non-inferior performance while reducing the second reader’s workload by 88%.

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The Royal College of Radiologists vice president for clinical radiology, Caroline Rubin, said, “Like the rest of the health service, breast imaging is understaffed and desperate for help. AI programmes will not solve the human staffing crisis as radiologists and imaging teams do far more than just look at scans, but they will undoubtedly help by acting as a second pair of eyes and a safety net. “We look forward to when AI can be used in practice, but it is a competitive market for developers and these programmes will need to be rigorously tested and regulated first.”

This AI system is capable of detecting cancers earlier than the standard of care and therefore reduce unnecessary recalls or biopsies.

This robust assessment of the AI system paves the way for prospective clinical trials to improve the accuracy and efficiency of breast cancer screening.

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