From research to diagnosis to treatment, AI has the potential to improve outcomes for some treatments by 30 to 40 percent and reduce costs by up to 50 percent. Although healthcare algorithms are predicted to represent a $42.5B market by 2026, less than 35 algorithms have been approved by the FDA, and only two of those are classified as truly novel.1 Obtaining the large data sets necessary for generalizability, transparency, and reducing bias has historically been difficult and time-consuming, due in large part to regulatory restrictions enacted to protect patient data privacy. That's why the University of California, San Francisco (UCSF) collaborated with Microsoft, Fortanix, and Intel to create BeeKeeperAI. It enables secure collaboration between algorithm owners and data stewards (for example, healthy systems, etc.) in a Zero Trust environment (enabled by Azure Confidential Computing), protecting the algorithm intellectual property (IP) and the data in ways that eliminate the need to de-identify or anonymize Protected Health Information (PHI)--because the data is never visible or exposed. By uncovering powerful insights in vast amounts of information, AI and machine learning can help healthcare providers to improve care, increase efficiency, and reduce costs.

From research to diagnosis to treatment, AI has the potential to improve outcomes for some treatments by 30 to 40 percent and reduce costs by up to 50 percent. Although healthcare algorithms are predicted to represent a $42.5B market by 2026, less than 35 algorithms have been approved by the FDA, and only two of those are classified as truly novel.1 Obtaining the large data sets necessary for generalizability, transparency, and reducing bias has historically been difficult and time-consuming, due in large part to regulatory restrictions enacted to protect patient data privacy. That's why the University of California, San Francisco (UCSF) collaborated with Microsoft, Fortanix, and Intel to create BeeKeeperAI. It enables secure collaboration between algorithm owners and data stewards (for example, healthy systems, etc.) in a Zero Trust environment (enabled by Azure Confidential Computing), protecting the algorithm intellectual property (IP) and the data in ways that eliminate the need to de-identify or anonymize Protected Health Information (PHI)--because the data is never visible or exposed. By uncovering powerful insights in vast amounts of information, AI and machine learning can help healthcare providers to improve care, increase efficiency, and reduce costs.

"You had to decide for every single patient how you're going to provide care for them in a way you never had before," he recalls. That prompted him to ponder the role of the physical itself: "What would happen if I delayed it three months, or didn't do it at all?" For Dr. Hyman and many other physicians and their patients, the pandemic triggered a disruption in one of medicine's most common encounters--and, through virtual visits, provided an early glimpse of the physical of the future. A look at how innovation and technology are transforming the way we live, work and play. An explosion of advances in digital technology, imaging, gene sequencing and artificial intelligence will likely transform the physical into an even more virtual experience.

In 1896, Italian physician Riva Rocci published the first of four papers on an invention that is still widely used. It was his take on the sphygmomanometer, a device to measure the pressure that a pumping heart exerts on the arteries. Rocci's basic approach of tying a cuff to the upper arm remains standard, and it is a vital tool because hypertension is one of the most serious medical ailments. The CDC reports that nearly half of all adults in the US have high blood pressure, and it is a primary or contributing factor in 500,000 deaths annually--it's like Covid-19 every year. Only a fourth of people with hypertension have it under control, in part because sphygmomanometers, whether used in a doctor's office or via clunky home units, don't supply a steady stream of readings, multiple times a day and in different settings, to help determine the proper treatment.

Machine learning experts working at Google Health have published a new study in tandem with the University of California San Francisco's (UCSF) computational health sciences department that describes a machine learning model the researchers built that can anticipate normal physician drug prescribing patterns, using a patient's electronic health records (EHR) as input. That's useful because around 2% of patients who end up hospitalized are affected by preventable mistakes in medication prescriptions, some instances of which can even lead to death. The researchers describe the system as working in a similar manner to automated, machine learning-based fraud detection tools that are commonly used by credit card companies to alert customers of possible fraudulent transactions: They essentially build a baseline of what's normal consumer behavior based on past transactions, and then alert your bank's fraud department or freeze access when they detect a behavior that is not in line with an individual's baseline behavior. Similarly, the model trained by Google and UCSF worked by identifying any prescriptions that "looked abnormal for the patient and their current situation." That's a much more challenging proposition in the case of prescription drugs versus consumer activity -- because courses of medication, their interactions with one another and the specific needs, sensitivities and conditions of any given patient all present an incredibly complex web to untangle.

Digital pathology specialist Proscia has been working hard to change the current pathology narrative. The Philadelphia, PA-based company has made several moves to accomplish this goal and its most recent move is a collaboration with the University of California, San Francisco (UCSF). Proscia builds software and has a platform for imaging workflow management that allows a pathologist in the lab to take digitized images of the glass slides the tissue biopsy sites on and move these images through workflows. The company uses its computational AI-based applications to find patterns in the imaging and transform that information into something meaningful for the pathologist. However, Proscia doesn't have a laboratory and that's where UCSF comes in, said Nathan Buchbinder, the firm's co-founder and Chief Product Officer.

Head CT is used worldwide to assess neurological emergencies and detect acute brain haemorrhages. Interpreting these head CT scans requires readers to identify tiny subtle abnormalities, with near-perfect sensitivity, within a 3D stack of greyscale images characterized by poor soft-tissue contrast, low signal-to-noise ratio and a high incidence of artefacts. As such, even highly trained experts may miss subtle life-threatening findings. To increase the efficiency, and potentially also the accuracy, of such image analysis, scientists at UC San Francisco (UCSF) and UC Berkeley have developed a fully convolutional neural network, called PatchFCN, that can identify abnormalities in head CT scans with comparable accuracy to highly trained radiologists. Importantly, the algorithm also localizes the abnormalities within the brain, enabling physicians to examine them more closely and determine the required therapy (PNAS 10.1073/pnas.1908021116).

A computer algorithm developed by scientists at the University of California, San Francisco (UCSF), and UC Berkeley bested two out of four expert radiologists at finding tiny brain hemorrhages in head scans -- an advance that one day may help doctors treat patients with traumatic brain injuries, strokes and aneurysms. Radiologists typically look at thousands of brain images each day, searching for tiny abnormalities that can signal life-threatening emergencies. A single, three-dimensional, computed tomography scan can produce a stack of 30 or more images, each of which must be reviewed by a radiologist. The researchers created their algorithm to see if artificial intelligence could more efficiently and accurately pick out images with significant abnormalities to help radiologists focus on the most important images and examine them more closely. "We wanted something that was practical, and for this technology to be useful clinically, the accuracy level needs to be close to perfect," said study co-author Esther Yuh, an associate professor of radiology at UCSF. "The performance bar is high for this application, due to the potential consequences of a missed abnormality, and people won't tolerate less than human performance or accuracy."

An algorithm developed by scientists at UC San Francisco and UC Berkeley did better than two out of four expert radiologists at finding tiny brain hemorrhages in head scans--an advance that one day may help doctors treat patients with traumatic brain injuries (TBI), strokes and aneurysms. The continued increase in diagnostic imaging studies, including 3D imaging studies such as computed tomography (CT), means that radiologists are looking at thousands of images each day, searching for tiny abnormalities that can signal life-threatening emergencies. The number of images from each brain scan can be so large that on a busy day, radiologists may opt to scroll through some large 3D stacks of images using mice with frictionless wheels, almost like viewing a movie. But it could be much more efficient--and potentially more accurate--if AI technology could pick out the images with significant abnormalities, so radiologists could examine them more closely. "We wanted something that was practical, and for this technology to be useful clinically, the accuracy level needs to be close to perfect," said Esther Yuh, MD, PhD, associate professor of radiology at UCSF and co-corresponding author of the study, published the week of Monday, Oct. 21, 2019, in Proceedings of the National Academy of Sciences (PNAS).

UC San Francisco is upping its research into advanced computing in healthcare, launching an artificial intelligence center specifically to advance its use in medical imaging. The Center for Intelligent Imaging will develop and apply artificial intelligence in the quest to find new ways to use radiology to look inside the body and to evaluate health and disease. UCSF investigators in the center will work with Santa Clara, Calif-based NVIDIA, which develops AI products to support infrastructure and tools. The collaboration will aim to create new ways to enable the translation of AI into clinical practice. "Artificial intelligence represents the next frontier for diagnostic medicine," says Christopher Hess, MD, chair of UCSF's Department of Radiology and Biomedical Imaging.