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Companies that make pharmaceutical, biologic and medical devices are caught hiding relevant information or purposefully delaying applying for patents at the U.S. Patent & Trademark Office (USPTO) in gamesmanship moves that occur in the medical field far more than in other industries, according to recent reports. Read More
Most predictive clinical decision support devices (CDS) that rely on machine learning (ML) or AI are approved and marketed without adequate proof that they deliver on their promise of improving outcomes for patients with critical illnesses, a study has found. Read More
A recent study of cardiac patients using a digital health solution found no long term impact on physical activity, demonstrating the difficulty of establishing the value of digital health technology. Read More
CDRH’s new development initiative — Total Product Life Cycle Advisory Program (TAP) — taps into an FDA goal: to ensure U.S. patients are first to benefit from innovative medical devices that, while developed quickly, are of high quality, safe and proven effective, and that include input from patients, providers and payers early in the process. Read More
An artificial intelligence platform can discern epileptic seizure patterns on electroencephalogram as well as an expert epileptologist, with nearly 100 percent accuracy, a finding that could extend expert diagnoses to clinics without access to specialists in epilepsy, according to a report in JAMA Neurology. Read More
Medical device companies that participate in the program can engage and collaborate with solutions providers and the FDA to demonstrate advanced technologies. Read More
The FDA and the Medical Device Innovation Consortium (MDIC) are collaborating to offer funding of up to $300,000 to medical devicemakers that use advanced technologies to enhance product quality or improve the manufacturing process. Read More
Scientists have long been perceived and portrayed in film as old people in white lab coats perched at a bench full of bubbling fluorescent liquids. The present-day reality is quite different. Scientists are increasingly data jockeys in hoodies sitting before monitors analyzing enormous amounts of data. Modern day labs are more likely composed of sterile rows of robots doing the manual handling of materials, and lab notebooks are now electronic in massive data centers holding vast quantities of information. Today, scientific input comes from data pulled from the cloud, with algorithms fueling scientific discovery the way bunsen burners once did. Read More
A medical school research team and a digital therapeutics firm in South Korea used deep learning (DL) models to detect and assess autism in videos of children as young as 24 months of age by measuring a set of behaviors called “joint attention.” Read More