From the NEJM Catalyst event AI and Machine Learning for Health Care Delivery, sponsored by Advisory Board, March 24, 2022. In the special artificial intelligence theme issue of NEJM Catalyst Innovations in Care Delivery, "Preventing Delayed and Missed Care by Applying Artificial Intelligence to Trigger Radiology Imaging Follow-up" explores a Northwestern Medicine initiative that uses recurrent neural networks and natural language processing to examine radiology reports for findings necessitating follow-up. Speaking at the NEJM Catalyst "AI and Machine Learning for Health Care Delivery" event, senior author Mozziyar Etemadi, MD, PhD, describes the In Depth article. Most people outside of health care associate radiology with images from X-rays, CT scans, and MRIs. But to doctors who are not radiologists, what comes to mind are large blocks of text from radiology reports, which can be a lot to parse through, Etemadi says.

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.

CHICAGO (Reuters) - A Google artificial intelligence system proved as good as expert radiologists at detecting which women had breast cancer based on screening mammograms and showed promise at reducing errors, researchers in the United States and Britain reported. The study, published in the journal Nature on Wednesday, is the latest to show that artificial intelligence (AI) has the potential to improve the accuracy of screening for breast cancer, which affects one in eight women globally. Radiologists miss about 20% of breast cancers in mammograms, the American Cancer Society says, and half of all women who get the screenings over a 10-year period have a false positive result. The findings of the study, developed with Alphabet Inc's (GOOGL.O) DeepMind AI unit, which merged with Google Health in September, represent a major advance in the potential for the early detection of breast cancer, Mozziyar Etemadi, one of its co-authors from Northwestern Medicine in Chicago, said. The team, which included researchers at Imperial College London and Britain's National Health Service, trained the system to identify breast cancers on tens of thousands of mammograms.

While there has been controversy over when and how often women should be screened for breast cancer using mammograms, studies consistently show that screening can lead to earlier detection of the disease, when it's more treatable. So improving how effectively mammograms can detect abnormal growths that could be cancerous is a priority in the field. AI could play a role in accomplishing that--computer-based machine learning might help doctors to read mammograms more accurately. In a study published Jan. 1 in Nature, researchers from Google Health, and from universities in the U.S. and U.K., report on an AI model that reads mammograms with fewer false positives and false negatives than human experts. The algorithm, based on mammograms taken from more than 76,000 women in the U.K. and more than 15,000 in the U.S., reduced false positive rates by nearly 6% in the U.S., where women are screened every one to two years, and by 1.2% in the U.K., where women are screened every three years.

To apply artificial intelligence to the task, the authors of the Nature report used mammograms from about 76,000 women in Britain and 15,000 in the United States, whose diagnoses were already known, to train computers to recognize cancer. Then, they tested the computers on images from about 25,000 other women in Britain, and 3,000 in the United States, and compared the system's performance with that of the radiologists who had originally read the X-rays. The mammograms had been taken in the past, so the women's outcomes were known, and the researchers could tell whether the initial diagnoses were correct. "We took mammograms that already happened, showed them to radiologists and asked, 'Cancer or no?' and then showed them to A.I., and asked, 'Cancer, or no?'" said Dr. Mozziyar Etemadi, an author of the study from Northwestern University. This was the test that found A.I. more accurate than the radiologists.

In the United States, only one radiologist reads the results and the tests are done every one to two years. In Britain, the tests are done every three years, and each is read by two radiologists. When they disagree, a third is consulted.'SUBTLE CUES'In a separate test, the group pitted the AI system against six radiologists and found it outperformed them at accurately detecting breast cancers.Connie Lehman, chief of the breast imaging department at Harvard's Massachusetts General Hospital, said the results are in line with findings from several groups using AI to improve cancer detection in mammograms, including her own work.The notion of using computers to improve cancer diagnostics is decades old, and computer-aided detection (CAD) systems are commonplace in mammography clinics, yet CAD programs have not improved performance in clinical practice.The issue, Lehman said, is that current CAD programs were trained to identify things human radiologists can see, whereas with AI, computers learn to spot cancers based on the actual results of thousands of mammograms.This has the potential to "exceed human capacity to identify subtle cues that the human eye and brain aren't able to perceive," Lehman added.Although computers have not been "super helpful" so far, "what we've shown at least in tens of thousands of mammograms is the tool can actually make a very well-informed decision," Etemadi said.The study has some limitations. Most of the tests were done using the same type of imaging equipment, and the U.S. group contained a lot of patients with confirmed breast cancers.Crucially, the team has yet to show the tool improves patient care, said Dr Lisa Watanabe, chief medical officer of CureMetrix, whose AI mammogram program won U.S. approval last year."AI

Researchers have used a deep-learning algorithm to detect lung cancer accurately from computed tomography scans. The results of the study indicate that artificial intelligence can outperform human evaluation of these scans. The condition is the leading cause of cancer-related death in the U.S., and early detection is crucial for both stopping the spread of tumors and improving patient outcomes. As an alternative to chest X-rays, healthcare professionals have recently been using computed tomography (CT) scans to screen for lung cancer. In fact, some scientists argue that CT scans are superior to X-rays for lung cancer detection, and research has shown that low-dose CT (LDCT) in particular has reduced lung cancer deaths by 20%.

Artificial intelligence is better than specialist doctors at diagnosing lung cancer, a US study suggests. The researchers at Northwestern University in Illinois and Google hope the technology could boost the effectiveness of cancer screening. Finding tumours at an earlier stage should make them easier to treat. The team said AI would have a "huge" role in the future of medicine, but the current software is not yet ready for clinical use. The study focused on lung cancer, which kills more people - 1.8 million a year - than any other type of cancer.

The condition is the leading cause of cancer-related death in the U.S., and early detection is crucial for both stopping the spread of tumors and improving patient outcomes. As an alternative to chest X-rays, healthcare professionals have recently been using computed tomography (CT) scans to screen for lung cancer. In fact, some scientists argue that CT scans are superior to X-rays for lung cancer detection, and research has shown that low-dose CT (LDCT) in particular has reduced lung cancer deaths by 20%. These errors typically delay the diagnosis of lung cancer until the disease has reached an advanced stage when it becomes too difficult to treat. New research may safeguard against these errors.

CHICAGO --- Deep learning - a form of artificial intelligence - was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists, reports a new study from Google and Northwestern Medicine. This deep-learning system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment. The deep-learning system was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists. Deep learning is a technique that teaches computers to learn by example.