QuantX recently became the first-ever computer-aided breast cancer diagnosis system cleared by the FDA for use in radiology, but it's not putting radiologists out of a job any time soon. "Radiology is the backbone of diagnosing many diseases today," said Jeffrey Aronin, chairman and CEO of Paragon Biosciences. "We believe the future is radiologists with technology." The combination of humans and machines apparently works really well. In a clinical study, QuantX helped radiologists interpret MRIs, noting the differences between cancerous and noncancerous breast lesions.

Elon Musk's secretive "brain-machine interface" startup, Neuralink, stepped out of the shadows on Tuesday evening, revealing its progress in creating a wireless implantable device that can – theoretically – read your mind. At an event at the California Academy of Sciences in San Francisco, Musk touted the startup's achievements since he founded it in 2017 with the goal of staving off what he considers to be an "existential threat": artificial intelligence (AI) surpassing human intelligence. Two years later, Neuralink claims to have achieved major advances toward Musk's goal of having human and machine intelligence work in "symbiosis". Neurolink says it has designed very small "threads" – smaller than a human hair – that can be injected into the brain to detect the activity of neurons. It also says it has developed a robot to insert those threads in the brain, under the direction of a neurosurgeon.

One of Ayush Alag's earliest memories is of biting into a chocolate bar with cashew nuts and suddenly feeling his throat get itchy. For most of his childhood, the Santa Clara, California resident avoided eating anything with cashews and other nuts that caused irritation as best as he could. By his middle school years, he and his parents wanted to know for sure: did he have a serious food allergy, like 32 million other Americans, or was it just a food sensitivity? They sought the help of an allergist, Joseph Hernandez of Stanford University. Hernandez told them that the difference between an allergy and a food sensitivity is huge.

Koios Medical, the leader in ultrasound diagnosis decision support software, announces its second 510(k) clearance from the U.S. Food and Drug Administration (FDA). Koios DS (Decision Support) Breast 2.0 is intended for use to assist physicians analyzing breast ultrasound images and aligns a machine learning generated probability of malignancy with the appropriate BI-RADS category. This milestone is an important step in advancing the company's mission of empowering physicians to improve diagnostic accuracy. Now cleared for use at the point of care (or connected to an image viewer for studies stored on PACS), Koios Medical's advancements represent a huge leap forward in using artificial intelligence in healthcare by bringing the power of deep learning to physicians' fingertips. Koios DS Breast 2.0 represents the most advanced AI-based diagnostic technology for ultrasound image analysis to date.

The joint effort comes as health systems are stepping up adoption and investment in data analytics, including predictive analytics and AI. In recent survey of CIOs, CTOs and chief analytics officers conducted by the Deloitte Center for Health Solutions, 84% said such technology will be extremely important to their organization's strategy over the next three years. Other healthcare sectors are investing in AI as well, giving rise to potential safety, efficacy and ethical issues as the technology is more frequently used. One year ago, FDA approved the first autonomous AI diagnostic system for sale in the U.S. The cloud-based IDx-DR software detects diabetic retinopathy in images taken by retinal cameras. And in February, Verily, the life sciences arm of Google parent Alphabet, launched an eye disease screening algorithm at Aravind Eye Hospital in Madurai, India.

With so many commercialized algorithms on the market, many health systems have an important decision to make: Should they buy an artificial intelligence (AI) model or build their own? If a health system elects to build its own model, it has to invest time and manpower into it. But the benefits could be tremendous. The case for building a customized AI model is simple: Instead of the algorithm learning on national data, it is learning on the health system's data, said Pamela Peele, Ph.D., chief analytics officer at UPMC insurance division and UPMC enterprises. She spoke during a World Health Care Congress keynote called "More than Buzz: Realize the Potential of AI and Machine Learning."

Often, large corporations – which may dominate a market sector like the medical imaging field, which includes computerized tomography (CT), magnetic resonance imaging (MRI) and radiography – are so large they are not able to adapt to new, even disruptive, technologies. Sometimes these are spun off into other companies, as is the case with Harris Corporation and AuthenTec, which developed the fingerprint technology used on Apple phones. Other times, a smaller, nimbler company may emerge that introduces something transformative. Such may be the case with Central Florida's Omega Medical Imaging led by Brian Fleming, which recently received FDA clearance for its FluoroShield system. EW: I want to talk about how you became CEO of Omega, but first, explain the FluoroShield system.

Scientists are developing a multitude of artificial intelligence algorithms to help radiologists, like this one that lights up likely pneumonia in the lungs. Artificial intelligence (AI) is poised to upend the practice of medicine, boosting the efficiency and accuracy of diagnosis in specialties that rely on images, such as radiology and pathology. But as the technology gallops ahead, experts are grappling with its potential downsides. "Just working with the technology, I see lots of ways it can fail," says Albert Hsiao, a radiologist at the University of California, San Diego, who has developed algorithms for reading cardiac images and improving their quality. One major concern: Most AI software is designed and tested in one hospital, and it risks faltering when transferred to another.

Lung and pancreatic cancers are very often difficult to treat, especially when the cancer has spread to other organs. Today, the five-year survival rate for lung cancer that has spread is 5%. Now a group of researchers have developed a system to use computers to increase a patient's odds. One of the biggest challenges in cancer treatment is catching it at an early stage before it has spread. "Something around 30% to 40% of cancers is missed during the early stages of screening," said Naji Khosravan, a PhD Candidate at the Center for Research in Computer Vision (CRCV), University of Central Florida.

SURGERY performed with the help of a robot has been billed as the next revolution in healthcare: such procedures can be carried out through an incision the width of a finger, causing less scarring and often allowing people to return to their homes more quickly. The UK's National Health Service recently announced plans to spend £50 million on more robotic surgical equipment for operating theatres, and yet the benefits of this high-tech approach are debated.