FDA


Healthcare Industry Will Stagnate Without AI – Know Why! - HIE Answers

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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. It enhances the digital healthcare experiences of patients by offering them conversational and personalized engagement. Moreover, doctor's efforts will be greatly supported, especially when conducting differential diagnosis and evidence-based treatment and precision medicine practice using artificial intelligence with IBM's Watson. Whether it is a voice-based medical intelligence system for remotely monitoring a patient, or diagnosing disease symptoms, or sending alerts for medical appointments and medications and more.


AI diagnostics are coming

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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. Unlike more-traditional vision software, where a programmer defines rules--for example, a stop sign has eight sides--in deep learning the algorithm finds the rules itself, but often without leaving an audit trail to explain its decisions. The FDA required Arterys to do extensive testing to make sure the results from its algorithm were on par with those generated by physicians. These covered 2,032 different diseases and included 1,942 images of confirmed skin cancers.


23andMe DNA test for Alzheimer's risk approved for sale in US

New Scientist

People in the US will soon be able to buy a genetic test that tells them how likely they are to develop 10 diseases, including late-onset Alzheimer's. But in 2013, the Food and Drug Administration banned 23andMe from offering a test that assessed a person's genetic risk for 254 disorders and conditions. The FDA was especially concerned by the test's assessment of breast cancer risk – a false positive might encourage a person to get unnecessary surgery, for instance, while a false negative might lead someone to be dismissive of breast cancer symptoms. The new test won't assess breast cancer risk, but will screen for Alzheimer's, Parkinson's and coeliac disease, and seven other disorders.


First FDA Approval For Clinical Cloud-Based Deep Learning In Healthcare

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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. 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.


Flipboard on Flipboard

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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. The solution was a system known as PHI Service which enables personal identifying information to be stripped from the imaging data at the point it is collected – generally a hospital. When accredited users of the system – doctors or other medical staff with authority to view personal records – log in, it grabs the imaging data and analytical results from Arterys's cloud, and the PHI from the hospital's secure server, and rebuilds it. The FDA's approval for Arterys solution is another important step forward.


First FDA Approval For Clinical Cloud-Based Deep Learning In Healthcare

Forbes

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. 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.


Riverain gets FDA approval of lung cancer detection software

AITopics Original Links

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. 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. A recent study of the new version of OnGuard by researchers at University of Chicago Medical Center found that the software identified 25 percent more lung cancers than radiologists found when reviewing the same X-rays without OnGuard. Riverain is readying a new "temporal comparison product," which compares two different chest X-rays done at different times, and then produces a third image to help radiologists study changes to a patient's condition, Worrell said.



Robot surgeon sews up pig intestines

PBS NewsHour

The Smart Tissue Autonomous Robot (STAR) can autonomously perform 60 percent of bowel anastomosis on pig intestines. But those hurdles have not stopped scientists at Children's National Medical Center's (CNMC) Sheikh Zayed Institute from developing a robotic system that has successfully sutured and reconnected portions of pig intestine in a living animal with little or no human intervention, according to a report in the May 4 Science Translational Medicine. CNMC's Smart Tissue Autonomous Robot (STAR)--although not yet completely autonomous--is designed to compensate for this by using a 3-D and near-infrared fluorescent imaging system, a force sensor and a preprogrammed algorithm to determine the appropriate type, tension and location of sutures. To test STAR's performance the researchers recruited surgeons to perform the identical procedure on pig intestines using three other methods: hand-sewn sutures, laparoscopy and the robot-assisted da Vinci Surgical System approved by the U.S. Food and Drug Administration in 2000.