The Arterys Cardio DLTM application is vendor agnostic and was developed using data from several thousand cardiac cases. The software produces editable automated contours, providing precise and consistent ventricular function in seconds. The trained deep learning algorithm was validated as producing results within an expected error range comparable to that of an experienced clinical annotator. This clearance enables Arterys to make use of its unique clinical annotation platform, which collects ground-truth data every time a user views a study on Arterys.com. In the future, the deep learning model can be optimized as new data is collected from all global users.

The first FDA approval for a machine learning application to be used in a clinical setting is a big step forward for AI and machine learning in healthcare and industry as a whole. Arterys's medical imaging platform has been approved to be put into use to help doctors diagnose heart problems. It uses a self-teaching artificial neural network which has learned from 1,000 cases so far, and will continue to improve its knowledge and understanding of how the heart works with each new case it examines. In order to be approved by the US Food and Drug Administration (FDA), it had to pass tests to show it can produce results at least as accurately as humans are currently able to. The key difference though is that Arterys takes an average of 15 seconds to produce a result for one case, which a professional human analyst would expect to spend between 30 minutes to an hour working on. Arterys was founded by Fabien Beckers, John Axerio-Cilies, Albert Hsiao and Shreyas Vasanawala when they met at Stanford University with a shared passion for the transformative potential of machine learning.

The first FDA approval for a machine learning application to be used in a clinical setting is a big step forward for AI and machine learning in healthcare and industry as a whole. Arterys's medical imaging platform has been approved to be put into use to help doctors diagnose heart problems. It uses a self-teaching artificial neural network which has learned from 1,000 cases so far, and will continue to improve its knowledge and understanding of how the heart works with each new case it examines. In order to be approved by the US Food and Drug Administration (FDA), it had to pass tests to show it can produce results at least as accurately as humans are currently able to. The key difference though is that Arterys takes an average of 15 seconds to produce a result for one case, which a professional human analyst would expect to spend between 30 minutes to an hour working on.

The first FDA approval for a machine learning application to be used in a clinical setting is a big step forward for AI and machine learning in healthcare and industry as a whole. Arterys's medical imaging platform has been approved to be put into use to help doctors diagnose heart problems. It uses a self-teaching artificial neural network which has learned from 1,000 cases so far, and will continue to improve its knowledge and understanding of how the heart works with each new case it examines. In order to be approved by the US Food and Drug Administration (FDA), it had to pass tests to show it can produce results at least as accurately as humans are currently able to. The key difference though is that Arterys takes an average of 15 seconds to produce a result for one case, which a professional human analyst would expect to spend between 30 minutes to an hour working on. Arterys was founded by Fabien Beckers, John Axerio-Cilies, Albert Hsiao and Shreyas Vasanawala when they met at Stanford University with a shared passion for the transformative potential of machine learning.

Analyzing queries made to Google, Bing, and other search engines can reveal the potentially dangerous consequences of mixing prescriptions before they are known to the Food and Drug Administration (FDA), according to a new study. Such data mining could even expose medical risks that slip through clinical trials undetected. Pharmaceuticals often have side effects that go unnoticed until they're already available to the public. This is especially true of side effects that emerge when two drugs interact, largely because drug trials try to pinpoint the effects of one drug at a time. Physicians have a few ways to hunt for these hidden risks, such as reports to FDA from doctors, nurses, and patients.

Patients can now guide robotic limbs using devices implanted in their brains. For the first time since accidents severed the neural connection between their brains and limbs, a small number of patients are reaching out and feeling the world with prosthetic devices wired directly to their brains. Earlier this month, scientists at the California Institute of Technology (Caltech) in Pasadena implanted a person's brain with electrode arrays that read neural activity to control a robotic arm and stimulate the brain to deliver a sensation of what the arm touched. And since 2011, a team at the University of Pittsburgh in Pennsylvania has been working with a small number of people who control prostheses through neural implants. "It's moving quick at the moment," says Christian Klaes, a neuroscientist on the Caltech effort.

Riverain Technologies has received regulatory approval for the next-generation version of its imaging software that "suppresses" bones to help radiologists detect cancerous lung nodules. OnGuard 5.2, the latest version of the software, uses pattern recognition and machine learning technologies to essentially allow radiologists to see behind ribs and clavicles that often obscure lung abnormalities. OnGuard also circles areas that may be a lung tumor, according to the Dayton, Ohio-area company. The software's aim is to help clinicians reading chest X-rays get better views of pulmonary nodules -- spots on the lungs that can be a form of early stage cancer, but can also be benign. The new version of the software offers greater sensitivity, meaning it can better detect nodules, and better specificity, meaning it yields fewer false positives, said Steve Worrell, Riverain's chief technology officer.

Its creators nicknamed it the "Luke Arm," after Luke Skywalker's ultra-advanced bionic limb. Now, after nearly eight years of development and testing, this robotic arm for amputees has been approved for commercialization by the U.S. Food and Drug Administration (FDA). The "Luke Arm," whose official name is DEKA Arm System, is one of the most advanced robotic prostheses ever built. According to the FDA, this is the first prosthetic arm approved by the agency that "translates signals from a person's muscles to perform complex tasks." The DEKA Arm was created by famed inventor Dean Kamen and his team at DEKA Research and Development Corp., in Manchester, N.H., as part of DARPA's Revolutionizing Prosthetics program.

Almost exactly a year ago, in April 2010, professional motocross rider Randy Childers sustained serious injuries after a crash in the last race of the day at Cowboy Badlands in West Beaumont, Texas. He suffered broken ribs and a fractured wrist, but most seriously a crushed vertebra in his neck (C3) that required him to be airlifted to Houston, where surgeons inserted an artificial vertebra and fused two others together (C4 and C5) during a marathon operation that lasted 12 hours. Today, the 24-year-old is the star in a single-patient trial of Rice University's RiceWrist robot, a wearable exoskeleton that mimics the joints from his shoulder to his hand. After months of traditional physical therapy, Childers had recovered enough by October to walk (albeit slowly) into the basement lab at Rice and begin to use the RiceWrist, which is built to reconnect motor pathways in the brain through repetitive movement. After just two weeks, Rice Professor Marcia O'Malley says, Childers was doing most of the work himself.

IBM Watson Health has announced a joint initiative with the US Food and Drug Administration to study the use of blockchain technology to share health data to ultimately improve public health. At first, the two-year collaboration will focus on oncology data, pulling together and exchanging data from a variety of sources including that from clinical trials, genomic data, EMRs, and from miscellaneous Internet of Things data from wearables, apps and connected devices. IBM and the FDA will look at how the technology can facilitate information exchange across a spectrum of data types, including clinical trials and real world data. For example, patient-generated data from connected devices could provide clinicians with more insights into population health, potentially offering up research opportunities and ways to leverage large quantities of data into biomedical and healthcare industries. At the core of the collaboration is blockchain technology, which allows secure data sharing between organizations more freely and has been increasingly favored among industry leaders.