An emerging artificial intelligence (AI) software that reportedly reduces false negative rates for fractures by 29 percent has received FDA clearance. BoneView AI (Gleamer) detects fractures on X-rays, highlights regions of interest and submits them to radiologists for confirmation, according to the French company Gleamer. The company said the algorithm was designed to aid a variety of physicians who read X-rays in clinical practice. Noting that traumatic injuries account for one-third of visits to emergency rooms (ERs), Gleamer noted that errors with fracture interpretation, which are common during evening hours, can represent up to 24 percent of harmful diagnostic errors in the ER. The company said these errors may result from fatigue and non-expert reading of X-rays.

Original article by Azfar Adib, who is currently pursuing his PhD in Electrical and Computer Engineering in Concordia University in Montreal. He is a Senior Member in the Institute of Electrical and Electronic Engineers (IEEE). A recent announcement by Meta about terminating the face recognition system in Facebook sparked worldwide attention. It comes as a sort of new reality for many Facebook users, who have been habituated for years to the automatic people recognition feature in Facebook photos and videos. Since the arrival of mankind on earth, facial outlook has remained as the most common identifier for humans.

Geoffrey Hinton is a legendary computer scientist . . . Naturally, people paid attention when Hinton declared in 2016, "We should stop training radiologists now, it's just completely obvious within five years deep learning is going to do better than radiologists." The US Food and Drug Administration (FDA) approved the first AI algorithm for medical imaging that year and there are now more than 80 approved algorithms in the US and a similar number in Europe. Yet, the number of radiologists working in the US has gone up, not down, increasing by about 7% between 2015 and 2019. Indeed, there is now a shortage of radiologists that is predicted to increase over the next decade.

Cardiologist and Us2.ai co-founder Dr Carolyn Lam talks to AIMed about the potential of AI to democratize heart ultrasound, her experience as an accidental entrepreneur, and the importance of championing women in cardiovascular science. You serve as a senior consultant cardiologist at the National Heart Centre Singapore, a full professor at Duke-National University of Singapore, and co-founder of Us2.ai. How do you split your time between these demanding positions? Time-wise I fortunately don't have to struggle since my time commitments are spelled out very clearly for me (days in clinics, days in clinical research, etc.); the challenge is really in staying ultra-focused on delivering my very best in the time that I have. To do that, I have had to learn the hard lesson of saying "no" – in fact my mission this year is to focus on my "not to do" list rather than on my "to do" list.

There is fair amount of excitement and hype about the ongoing emergence of artificial intelligence (AI) and the potential promise of the technology in improving diagnostic accuracy and increasing workflow efficiencies in radiology. However, as Nina Kottler, MD, points out in a recent video interview with Diagnostic Imaging, there are challenges as well when it comes to assessment and implementation of AI into one's practice. While there are "hundreds of FDA-cleared and approved algorithms in radiology alone," she notes that it is "early on in the maturity of the technology of AI with respect to health care" so choosing the right AI vendor for your practice is critical. While technical prowess is important, 80 percent of what an AI vendor does is help create the workflow around the algorithm to ensure it works well, according to Dr. Kottler, the Associate Chief Medical Officer for Clinical AI and VP of Clinical Operations at Radiology Partners. Dr. Kottler says cultural alignment is an important consideration as you are seeking a vendor that values your input as a radiologist and is on a similar wavelength with you on future directions of AI in radiology. For pertinent insights on the assessment and implementation of AI technology, watch the video below.

The growing involvement of technologies such as AI and computer vision in healthcare enables health experts to predict and track the advancement of Alzheimer's disease in patients. The mere possibility of being diagnosed with Alzheimer's is enough to fill patients' minds with a deep sense of foreboding. After all, this is a disease that increasingly limits the functioning of a patient's brain, leading them, eventually, into a perpetually vegetative state of existence. In 2021, one in nine persons in the US aged 65 and older are living with Alzheimer's dementia. The progression of Alzheimer's in a patient is closely linked with their age, and hence, at least for now, there is no known cure for it.

As scientists make strides in finding answers about COVID-19, artificial intelligence has aided one Michigan State University researcher and his team in finding answers about the new omicron variant. The MSU researchers report omicron and other variants are evolving increased infectivity and antibody resistance according to an artificial intelligence model. Therefore, new vaccines and antibody therapies are needed, the researchers say. Understanding how SARS-CoV-2 evolves is essential to predicting vaccine breakthrough and designing mutation-proof vaccines and monoclonal antibody treatments. In a recent study in American Chemical Society Infectious Diseases, Guowei Wei, professor in MSU's Departments of Mathematics as well as Electrical and Computer Engineering, and colleagues, analyzed almost 1.5 million SARS-CoV-2 genome sequences taken from people with COVID-19.

Sirona Medical, a company developing an "operating system" for digital radiology, has acquired Nines– a company that has developed FDA-cleared analysis and triage algorithms. This acquisition comes during a somewhat shaky moment in AI-based radiology. But Sirona is betting this move proves out its thesis: to bring AI into the clinical workflow, we need to rebuild things from the ground up. To understand where Sirona and Nines fit together, think about the IT behind radiology as a layer cake. The first layer of that cake is made of medical image databases.

With 5G, real-time computing will become a reality. The high speeds, high data throughput, and high number of connections that 5G enables will effectively erase the lag time between when data gets generated to when we can act on it. And, while self-driving vehicles might be the most visible new example of real-time processing most of us see, they are really only the tip of the iceberg, especially as private networks and network slicing roll out to bring the power of carrier-grade infrastructure to more locations and situations. IDC estimates that real-time data will grow by 50 times between 2000 and 2030 and constitute 30% of all data by then. Manufacturing will be one of the first places where a real-time data revolution takes place.

If not previously completed, a background security investigation will be required for all appointees. Appointment will be subject to the applicant's successful completion of a background security investigation and favorable adjudication. Failure to successfully meet these requirements may be grounds for appropriate personnel action. In addition, if hired, a background security reinvestigation or supplemental investigation may be required at a later time. Applicants are also advised that all information concerning qualifications is subject to investigation. False representation may be grounds for non-consideration, non-selection, or appropriate disciplinary action.