Google Health has teamed up with Northwestern Medicine to explore whether artificial intelligence (AI) could prioritise reviews of mammograms with a higher suspicion of breast cancer. Women whose mammograms show a higher likelihood of breast cancer might be able to be seen the same day for follow up, according to a statement from Northwestern Medicine. Dr Sarah Friedewald, associate professor of radiology at Northwestern University Feinberg School of Medicine, said: "With the use of artificial intelligence, we hope to expedite the process to diagnosis of breast cancer by identifying suspicious findings on patients' screening examinations earlier than the standard of care. The Goolge-funded study builds on research conducted by Northwestern Medicine, Google Health and the NHS in 2020, which found AI screening of mammograms was as accurate as human experts. Dr Mozziyar Etemadi, research assistant professor of anesthesiology at Northwestern Medicine, added: "This study is the next step by applying the AI models in a prospective study to better understand how AI can be the most helpful for clinicians and patients in the real world."

A team at Google has developed an artificial intelligence program aimed at helping doctors accurately detect cancer in mammograms. Thousands of women receive a false negative on their breast cancer tests each year, while one in 10 receive a false positive. Shravya Shetty, who heads the Google team developing the system, told CBS News' Jamie Yuccas that their AI model reduced false positives by almost 6% and false negatives by about 9%. Shetty also claimed that it caught suspicious tissues on mammograms missed by the human eye. Interventional radiologist Dr. Susan Drossman predicted that the AI program would be integrated into her and other doctors' work stations "probably within the next year."

Artificial intelligence can diagnose breast cancer more accurately than trained doctors, a study suggests. The research on almost 30,000 women who underwent screening found a computer programme could reduce the number of cases missed by more than two thirds. Researchers said the algorithmdeveloped by Imperial College London, Northwestern University in Chicago and Google Health was a "huge advance" in early detection of cancers. Breast cancer is the most common type of cancer in the UK, affecting around one in eight women - with 55,000 diagnoses annually and 11,000 deaths. Experts said the breakthrough could save thousands of lives, by finding deadly tumours that would otherwise go undetected.

UCLA researchers have developed an artificial intelligence system that could help pathologists read biopsies more accurately and to better detect and diagnose breast cancer. The new system, described in a study published today in JAMA Network Open, helps interpret medical images used to diagnose breast cancer that can be difficult for the human eye to classify, and it does so nearly as accurately or better as experienced pathologists. "It is critical to get a correct diagnosis from the beginning so that we can guide patients to the most effective treatments," said Dr. Joann Elmore, the study's senior author and a professor of medicine at the David Geffen School of Medicine at UCLA. A 2015 study led by Elmore found that pathologists often disagree on the interpretation of breast biopsies, which are performed on millions of women each year. That earlier research revealed that diagnostic errors occurred in about one out of every six women who had ductal carcinoma in situ (a noninvasive type of breast cancer), and that incorrect diagnoses were given in about half of the biopsy cases of breast atypia (abnormal cells that are associated with a higher risk for breast cancer).

A computer could be better than a doctor at diagnosing certain types of cancerous and precancerous breast lesions, new research suggests. Researchers at the University of California, Los Angeles, trained an artificial intelligence system using 240 biopsy images, and tested it against 87 pathologists. The machine performed more or less as well as doctors at detecting and classifying all of the breast biopsies. However, it was better at making one crucial distinction: telling the difference between DCIS (ductal carcinoma in situ), a type of cancer, and atypical hyperplasia, a high-risk lesion that has very similar hallmarks but does is not cancerous and does not require the same level of treatment. 'Medical images of breast biopsies contain a great deal of complex data and interpreting them can be very subjective,' said Dr Joann Elmore, lead author of the study published in the JAMA Network Open journal.

Several European companies have trained artificial intelligence to detect signs of breast cancer in scans. Several European companies have trained artificial intelligence (AI) to detect signs of breast cancer, in an effort to help hospitals contend with a shortage of radiologists. The U.K.'s Kheiron Medical, the latest company to announce it will use AI algorithms to try to diagnose breast cancer, launched a trial on historic scans at a National Health Service trust in Leeds. Kheiron's algorithms were trained on 500,000 scans from hospitals in Hungary. The company submitted its findings for peer review after concluding its technology beat the average performance of a human radiologist when tested against 3,500 scans.

A high school junior has created a computer brain that can diagnose breast cancer with 99 percent sensitivity. Seventeen-year-old Brittany Wenger of Sarasota, Fla., wrote a breast cancer-diagnosing app based on an artificial neural network, basically a computer program whose structure is inspired by the way brain cells connect with one another. She won grand prize at the Google Science Fair for her invention in ceremony held in Palo Alto, Calif. Like other artificial intelligence programs, artificial neural networks "learn" what to do by analyzing examples they're given and they perform better if they get more examples. In addition, they're able to detect patterns in data that are too complex for human brains or other types of programs to analyze.