Machines giving suggestions to doctors about treating their patients, a scene straight from a sci-fi movie and a fantasy of the healthcare community, is becoming a reality now. In a first of its kind move, the US Food and Drug Administration (FDA) recently gave green signal to market IDx-DR, a medical device to detect diabetic retinopathy, a disease in which high blood sugar damages blood vessels in the retina of eyes and leads to the vision loss. Made by the US-based IDx LLC, the software analyses images of the eye taken by a camera and tells the doctor accurately about the extent of the disease, called as'diabetic retinopathy'. Approved last month, this is the first approval in the area of artificial intelligence that can potentially replace a specialized doctor to interpret medical imagery and decide on the medical outcome. This week, the FDA gave go ahead to Boston-based Beta Bionics, which has a partnership with Novo Nordisk, to test its autonomous bionic pancreas that employs artificial intelligence to vary hormone doses in adults and children with Type 1 diabetes.

Today, the U.S. Food and Drug Administration permitted marketing of Imagen OsteoDetect, a type of computer-aided detection and diagnosis software designed to detect wrist fractures in adult patients. "Artificial intelligence algorithms have tremendous potential to help health care providers diagnose and treat medical conditions," said Robert Ochs, Ph.D., acting deputy director for radiological health, Office of In Vitro Diagnostics and Radiological Health in the FDA's Center for Devices and Radiological Health. "This software can help providers detect wrist fractures more quickly and aid in the diagnosis of fractures." The OsteoDetect software is a computer-aided detection and diagnostic software that uses an artificial intelligence algorithm to analyze two-dimensional X-ray images for signs of distal radius fracture, a common type of wrist fracture. The software marks the location of the fracture on the image to aid the provider in detection and diagnosis.

Andy Dufresne, the wrongly convicted character in The Shawshank Redemption, provocatively asks the prison guard early in the film: "Do you trust your wife?" It's a dead serious question regarding avoiding taxes on a recent financial windfall that had come the guard's way, and leads to events that eventually win freedom for Andy. And it's also a dead serious question being asked today with respect to AI. At this point we all recognize that successful deployment of AI is going to come down to something much more fundamental than the technical aspects of algorithms, neural networks and machine learning. It's going to come down to trust. Do we trust the black box calculations of AI? Do we trust it to drive our cars, diagnose our illnesses, and manage our finances? We have the same issue of trust with objects, but with a different set of circumstances.

Human biology is full of surprises -- especially for drug makers. Viagra wasn't designed for erectile dysfunction. Both drugs were meant to treat cardiovascular issues (as sildenafil and minoxidil, respectively), until patients reported their sexual and follicular side effects. When his son was diagnosed with an ultra-rare disease, computer scientist Matt Might, Ph.D., kicked off a search for answers. His quest led to partnerships with researchers across the country, a White House appointment, a faculty position at Harvard, and a profile in the New Yorker.

The need for new medications is higher than ever, but so is the cost and time to bring them to market. Developing a new drug can cost billions and take as long as 14 years, according to the U.S. Food and Drug Administration. Yet with all that effort, only 8 percent of drugs make it to market, the FDA said. "We need to make smarter decisions about which potential medicines we develop and test," said Abraham Heifets, co-founder of San Francisco-based startup Atomwise. The six-year-old company, a member of our Inception startup incubator program, is working to make that happen by using GPU-accelerated deep learning to predict which molecules are most likely to lead to treatments.

Machine-learning algorithms accomplish tasks by training on a set of data, rather than being programmed by humans. Armed with the knowledge of what worked before, the system instructs the implant to stimulate users' brains to interrupt a seizure at its onset. The innovation is part of a larger phenomenon that has big implications for how we identify and treat disease: the introduction of artificial intelligence to consumer and clinical electronics. As machines learn from at times millions of humans, doctors are gaining the ability to better identify disease and even predict it before it becomes catastrophic. As in every other area of human endeavor, the introduction of AI to medicine comes with challenges.

Machine-learning algorithms accomplish tasks by training on a set of data, rather than being programmed by humans. Armed with the knowledge of what worked before, the system instructs the implant to stimulate users' brains to interrupt a seizure at its onset. The innovation is part of a larger phenomenon that has big implications for how we identify and treat disease: the introduction of artificial intelligence to consumer and clinical electronics. As machines learn from at times millions of humans, doctors are gaining the ability to better identify disease and even predict it before it becomes catastrophic. As in every other area of human endeavor, the introduction of AI to medicine comes with challenges.

An artificial intelligence algorithm is able to identify patients with congenital Long QT syndrome using limited electrocardiogram data, according to an abstract published May 10 at the Heart Rhythm Scientific Sessions conference in Boston. LQTS is a heart rhythm disorder that, if left untreated, puts patients at an increased risk for arrhythmias and sudden cardiac death. For the study, a team of researchers from Rochester, Minn.-based Mayo Clinic and AliveCor sought to determine whether AI could identify patients with congenital LQTS despite patients having a normal QTc on their EKG. The researchers applied an AI algorithm that used deep neural networks to patient data from lead I of a 12-lead EKG and evaluated whether the algorithm was able to distinguish between patients with concealed LQTS and those without the condition. The researchers found the deep neural network achieved 79 percent accuracy, along with specificity of 81 percent and sensitivity of 73 percent.

In the last several weeks, FDA Commissioner Scott Gottlieb has commented on artificial intelligence and the future of medicine. For nearly a decade, many visionaries and innovators in medicine have believed this to be the case. Finally, the FDA is getting on board by creating an internal data science incubator, called Information Exchange and Data Transformation, or INFORMED. Already this year, the FDA has approved a clinical support software that uses AI algorithms to detect changes in brain function that can alert neurologists much faster than traditional technologies. In addition, the agency also approved an AI-based tool that uses a camera to detect diabetic retinopathy -- a condition that can lead to blindness if not detected and treated early.

Many of you have probably been to France, considering that it's the most visited country in the world. While you were there, you may have noticed that the country is quite large – about the size of Texas, but with far fewer Mexicans. Like Texans, the French are proud of where they come from, and will ramble on about it in their funny little accents much to the chagrin of anyone within earshot. This sense of national pride is what made our article on "The Top-10 French Artificial Intelligence Startups" quite popular, and as we promised, we're going to follow up with an article on some French AI startups in healthcare. None of the top French AI companies we wrote about last month came from the healthcare sector, which happens to be the cornerstone of Monsieur Macron's $1.8 billion AI investment program over the next four years.