The Food and Drug Administration is creating a digital health unit within its Center for Devices and Radiological Health in an effort to develop internal technical expertise, and streamline the agency's software review process and regulation of medical devices. "Because it's such an emerging area, having a centralized unit in the Center Director's Office is important for coordination on digital health topics and having consistency in applying policies," says Bakul Patel, associate director of digital health in the FDA's CDRH. Also See: FDA's medical device arm ramps up HIT strategies The digital health unit will be established in the CDRH's Office of the Center Director as part of the next iteration of the Medical Device User Fee Amendments, under which the FDA is authorized to collect user fees from medical device manufacturers. In exchange for those fees, FDA commits to meeting certain performance goals, such as reviewing submissions within specified timeframes. The medical device users fees, which must be reauthorized every five years, expire in September.

The healthcare sector is one of those sectors that has always embraced emerging technologies to make better use of technological innovations. And now artificial intelligence (AI) is gradually making its way into the healthcare market with all its power to disrupt. The annual investment in artificial intelligence for healthcare will grow tenfold in the next five years, becoming a $6-billion industry by 2021 – estimates Frost & Sullivan. They have also forecasted that by 2025, AI systems could be involved in everything from population health management to digital avatars capable of answering specific patient queries. In healthcare, the opportunity for AI is not just limited to making doctors and medical providers more competent in their work; in fact, it's about saving lives and making the lives of the patients better.

One of the more interesting panels at last week's Health Datapalooza featured four speakers involved in the application of artificial intelligence to healthcare, including the creation of predictive models. In areas involving massive amounts of information in the diagnostic and genomic space, machine learning is already in use today, and the FDA is starting to approve applications of deep learning. For instance, a company called Arterys recently won FDA approval for its Cardio DL application, which uses deep learning to automate time-consuming analyses and tasks that are performed manually by clinicians today. Although they each come at it from a different angle based on their company's focus, there were several overarching themes the Datapalooza panelists tackled about the application of algorithms in healthcare, including the importance of transparency to getting clinician engagement. Getting buy-in from clinicians is a huge challenge, said Eric Just, a senior vice president for product development at Health Catalyst, which builds analytics and decision support tools for its health system customers.

Tia Geri is the shortest player on her club soccer team. Geri, who turned 17 last month, has been playing with the same group of girls for almost as long as she's been living with type 1 diabetes. And while she's not the only one on the team with the disease, she is the only one with an artificial pancreas--a computer system that can control her insulin levels without her telling it to. A sensor on her abdomen monitors the glucose in her blood, and a pump adds the insulin her body needs to turn that sugar into energy. Geri is one of the first people in the country to get the MiniMed 670G, the first bionic pancreas to be approved by the US Food and Drug Administration.

Earlier this year, artificial intelligence scientist Sebastian Thrun and colleagues at Stanford University demonstrated that a "deep learning" algorithm was capable of diagnosing potentially cancerous skin lesions as accurately as a board-certified dermatologist. The cancer finding, reported in Nature, was part of a stream of reports this year offering an early glimpse into what could be a new era of "diagnosis by software," in which artificial intelligence aids doctors--or even competes with them. Experts say medical images, like photographs, x-rays, and MRIs, are a nearly perfect match for the strengths of deep-learning software, which has in the past few years led to breakthroughs in recognizing faces and objects in pictures. Companies are already in pursuit. Verily, Alphabet's life sciences arm, joined forces with Nikon last December to develop algorithms to detect causes of blindness in diabetics.

Recently we talked before how big data is the new frontier with just .05% of all data available today having been analyzed. This means that all kinds of gold prospectors are lining up with their freshly crafted artificial intelligence (AI) algorithms looking to extract all the value they can from this wild west of data before someone else does. Perhaps nowhere is there more excitement at the moment than the applications to be had in the healthcare industry. Here's a look at just some of the startups that are applying artificial intelligence and big data to healthcare (courtesy of the bright minds over at CB Insights): The application that we've circled above is "drug discovery" using AI or what's also known as "computational drug discovery". The reason that this is now a thing is not just because of all the big data that's available now, but also because of how cheap cloud computing has become, not to mention the emergence of deep learning algorithms.

Existing VR systems and experiences are immersive, engaging and sometimes even interactive. But they don't offer a quick, easy way for you to express your emotions. Medical device maker MindMaze has come up with a novel, compelling way to convey your facial expressions in VR called Mask. It's a foam insert that's compatible with existing headsets and uses diodes to read your biosignals and muscles. The potential applications here are plenty: You could deduce, from your opponents' faces, when they're preparing to shoot or see a new acquaintance laugh at your joke in social VR scenarios.

Developing new drugs is notoriously inefficient. Fewer than 12 percent of all drugs entering clinical trials end up in pharmacies, and it costs about $2.6 billion to bring a drug to market. There are so many molecules to test that pharmaceutical researchers use pipetting robots to test a few thousand variants all at once. The best candidates then go into animal models or cell cultures, where hopefully a few will go on to bigger animal and human clinical trials. Which is why more and more drug developers are turning to computers and artificial intelligence to narrow down the list of potential drug molecules--saving time and money on those downstream tests.

Deep learning has been used to transform artificial intelligence (AI) development, whether it is from beating players in games like Go or poker to improving self-driving AI. But perhaps the most important changes for most of us is how AI advances and machine learning are affecting healthcare. In January, a medical startup won FDA approval for an AI-assisted cardiac imaging system called Arterys, and AI is playing vital roles in other health fields such as fighting cancer and aging. NVIDIA boasts that with deep learning, "AI can help doctors make faster, more accurate diagnoses. It can predict the risk of a disease in time to prevent it."

The world knows no deadlier assassin than heart disease. It accounts for one in four fatalities in the US. Early detection remains the key to saving lives, but catching problems at the right time too often relies upon dumb luck. The most effective way of identifying problems involves an EKG machine, a bulky device with electrodes and wires. Even many portable machines like battery-powered Holter monitors, are unwieldy.