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The project began with a vexing problem. Imaging tests that turned up unexpected issues -- such as suspicious lung nodules -- were being overlooked by busy caregivers, and patients who needed prompt follow-up weren't getting it. After months of discussion, the leaders of Northwestern Medicine coalesced around a heady solution: Artificial intelligence could be used to identify these cases and quickly ping providers. If only it were that easy. It took three years to embed AI models to flag lung and adrenal nodules into clinical practice, requiring thousands of work hours by employees who spanned the organization -- from radiologists, to human resources specialists, to nurses, primary care doctors, and IT experts. Developing accurate models was the least of their problems.

Artificial Intelligence could soon help doctors flag problematic breast screenings in just one visit, with new technology being tested at Northwestern University in coming months. According to Northwestern Medicine, the new AI technology will be tested on a group of approximately 2,000 patients, with the stated goal of finding irregularities and helping with treatment decisions and outcomes. Google unveiled the technology earlier this week as Northwestern Medicine launched the study. Northwestern Medicine's Dr. Sarah Friedewald, Chief of Breast Imaging, says the goal is to find problems faster and in fewer visits. "Their algorithm was able to detect more cancers than the radiologist," said Dr. Friedewald.

A new research study from Northwestern Medicine and Google will explore whether Artificial Intelligence (AI) models can reduce the time to diagnosis for women whose mammograms show a higher likelihood of breast cancer. The trial will evaluate if investigational AI models could help by prioritizing radiologist review of mammogram images with a higher suspicion of breast cancer. Digital mammography, or X-ray imaging of the breast, is the most common method to try to catch breast cancer as early as possible, with approximately 40 million exams performed each year in the U.S. In the current system, women go to the clinic for their mammogram and then 10% to 15% of them will require an additional diagnostic workup. This can take days or even weeks and requires at least two trips to the clinic for the patient, often resulting in added worry during that period of waiting. The AI model is trained to quickly find the mammograms which need further review and prioritize these for the radiologist to review.

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."

Google AI researchers working with Northwestern Medicine created an AI model capable of detecting lung cancer from screening tests better than human radiologists with an average of eight years experience. When analyzing a single CT scan, the model detected cancer 5% more often on average than a group of six human experts and was 11% more likely to reduce false positives. Humans and AI achieved similar results when radiologists were able to view prior CT scans. When it came to predicting the risk of cancer two years after a screening, the model was able to find cancer 9.5% more often compared to estimated radiologist performance laid out in the National Lung Screening Test (NLST) study. Detailed in research published today in Nature Medicine, the end-to-end deep learning model was used to predict whether a patient has lung cancer, generating a patient lung cancer malignancy risk score and identifying the location of the malignant tissue in the lungs.

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.

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.

Bluhm Cardiovascular Institute at Northwestern Medicine announced this week that it has been doing new artificial intelligence work in an effort to improve the efficacy and accuracy of its cardiac screening. Clinicians there are using a cardiac monitoring platform from Eko, studying how its AI-enabled digital stethoscopes can interpret heart sounds to help screen for heart murmurs and valvular damage. They depend on a "highly trained musical ear that can separate subtle abnormalities from normal sounds with cardiologist-level precision," according to Northwestern researchers. The idea with the Eko stethoscopes is that AI and machine learning can combine the data from tens of thousands of heart sound patterns to help clinicians better assess what sounds are normal and what's not. "One of the biggest problems in healthcare is that general practitioners so often miss heart murmurs that if found earlier would allow patients to get treatment before problems arise," said Connor Landgraf, CEO of Eko, in a statement.