When Bakul Patel started as a policy advisor in the US Food and Drug Administration in 2008, he could pretty much pinpoint when a product was going to land in front of the reviewers in his division. Back when medical devices were heavy on the hardware--your pacemakers and your IUDs--it would take manufacturers years to get them ready for regulatory approval. FDA reviewers could keep up pretty well. But as computer code took on more complex tasks, like spotting specious moles and quantifying blood flow, their duties began to accelerate. Software developers needed months, not years, to make it to the market.

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.

Tech companies and investors have recently been piling money into artificial intelligence--and plenty has been trickling down to chip maker Nvidia. The company's revenues have climbed as it has started making hardware customized for machine-learning algorithms and use cases such as autonomous cars. At the company's annual developer conference in San Jose, California, this week, the company's CEO Jensen Huang spoke to MIT Technology Review about how the machine-learning revolution is just starting. Nvidia has benefitted from a rapid explosion of investment in machine learning from tech companies. Can this rapid growth in the use cases for machine learning continue?

Tech companies and investors have recently been piling money into artificial intelligence--and plenty has been trickling down to chip maker Nvidia. The company's revenues have climbed as it has started making hardware customized for machine-learning algorithms and use cases such as autonomous cars. At the company's annual developer conference in San Jose, California, this week, the company's CEO Jensen Huang spoke to MIT Technology Review about how the machine-learning revolution is just starting. Nvidia has benefitted from a rapid explosion of investment in machine learning from tech companies. Can this rapid growth in the use cases for machine learning continue?

It's the question on everyone's mind when reading Siddhartha Mukherjee's article in the New Yorker "AI vs MD", which pits human doctors against computers. It's a beautifully composed summary on the role of software algorithms in healthcare, examining the issue through the most controversial lens. Perhaps some radiologists read it and had an existential crisis. Surely some machine learning Ph.D.s read it and rolled their eyes with the gross simplicity with which deep learning was described. It's a shocking and provocative concept for any reader of The New Yorker – doctors are the best and brightest among us, even they could be outsmarted by a machine?

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.

When she started in vitro fertilization, Katie Shepard, a medical device consultant from outside St. Paul, Minnesota, knew it could take more than one round to get pregnant. So, after the grueling regimen of hormone injections, ultrasound exams, egg retrieval and transfer of embryos back into her womb, she stayed optimistic -- until her second cycle. Of the 25 eggs harvested over the course of those two IVF treatments, only three developed into embryos. "It felt like someone took me out at the knees with a baseball bat," Shepard says. Worse, the embryos didn't take, nor did any from her third cycle.

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.