The multi-limbed da Vinci can be utilized in a variety of procedures, including cardiovascular, colorectal, gynaecological, head and neck, thoracic and urologic medical procedures, however, only if they're minimally invasive. How large the market could be is as yet hazy, yet experts concur the potential still can't seem to be tapped. So more players are moving in, and rapidly. As the beginning of robotic surgery offers an approach to increasingly precise control and better patient results, early pioneers like Intuitive Surgical Inc. are seeing increased pressure from large organizations like Johnson and Johnson and Medtronic PLC, which have made major M&A investments to break into the market as of late. Intuitive's da Vinci system was first affirmed by the U.S. Food and Drug Administration in 2000 for urology.
During the past decade, deep learning has seen groundbreaking developments in the field of AI (Artificial Intelligence). But what is this technology? And why is it so important? Well, let's first get a definition of deep learning. Here's how Kalyan Kumar, who is the Corporate Vice President & Chief Technology Officer of IT Services at HCL Technologies, describes it: "Have you ever wondered how our brain can recognize the face of a friend whom you had met years ago or can recognize the voice of your mother among so many other voices in a crowded marketplace or how our brain can learn, plan and execute complex day-to-day activities? The human brain has around 100 billion cells called neurons. These build massively parallel and distributed networks, through which we learn and carry out complex activities. Inspired from these biological neural networks, scientists started building artificial neural networks so that computers could eventually learn and exhibit intelligence like humans."
The number of people testing positive for coronavirus has spiked after testing has become more readily available in the U.S. President Trump and Gov. Cuomo are just two officials considering how to address this issue. As coronavirus testing in the U.S. continues to lag behind that in other highly affected countries such as South Korea, several domestic startups are reportedly launching the first at-home tests. The products have been greenlit by the U.S. Food and Drug Administration under new expedited guidelines to help combat the virus, according to Stat, a health care industry news outlet. One such test provider is Nurx, a San Francisco-based company best known for home testing products for birth control and for sexually transmitted infections, now offers testing kits for COVID-19 through the mail for $181 after prospective customers fill out an online form with their symptoms. WHO IS STEPHEN HAHN, FDA COMMISSIONER?
WIRE)-- Canon Medical Systems USA, Inc. has received 510(k) clearance on its Advanced intelligent Clear-IQ Engine (AiCE) for the Vantage Galan 3T MR system, further expanding access to its new Deep Learning Reconstruction (DLR) technology. This technology, which is also available across a majority of Canon Medical's CT product portfolio, uses a deep learning algorithm to differentiate true MR signal from noise so that it can suppress noise while enhancing signal, forging a new frontier for MR image reconstruction. AiCE was trained using vast amounts of high-quality image data, and features a deep learning neural network that can reduce noise and boost signal to quickly deliver sharp, clear and distinct images, further opening doors for advancements in MR imaging. "AiCE utilizes a next generation approach to MR image reconstruction, further proving Canon Medical's leadership and commitment to innovation in diagnostic imaging," said Jonathan Furuyama, managing director, MR Business Unit, Canon Medical Systems USA, Inc. "With the expansion of this unique DLR method across modalities and into MR, we're elevating diagnostic imaging capabilities for our customers by bringing the power of AI to routine imaging to provide more possibilities in improving patient care than ever before." Canon Medical Systems USA, Inc., headquartered in Tustin, Calif., markets, sells, distributes and services radiology and cardiovascular systems, including CT, MR, ultrasound, X-ray and interventional X-ray equipment.
Last month, the federal Food and Drug Administration (FDA) approved nearly half a dozen of their algorithms designed to detect heart murmurs and atrial fibrillation, irregular heartbeats that could lead to stroke or blood clots. And in December, the FDA granted a "breakthrough" device designation to an algorithm that analyzes data from the heart's electrical impulses for evidence of heart failure. Such a designation allows the agency to fast track significant innovations for approval.
The rapid entry of artificial intelligence is stretching the boundaries of medicine. It will also test the limits of the law. Artificial intelligence (AI) is being used in health care to flag abnormalities in head CT scans, cull actionable information from electronic health records, and help patients understand their symptoms. At some point, AI is bound to make a mistake that harms a patient. When that happens, who -- or what -- is liable?
Caption Health, a leading medical AI company, announced that its flagship product, Caption AI, the first AI-guided medical imaging acquisition system, is now available for pre-order by healthcare providers. Caption AI is a transformational new technology that enables healthcare practitioners--even those without prior ultrasound experience--with the ability to perform ultrasound exams quickly and accurately, by providing expert guidance, automated quality assessment, and intelligent interpretation capabilities. Caption AI comes equipped with Caption Guidance software, which uses artificial intelligence to provide real-time guidance and feedback on image quality to enable capture of diagnostic quality images. This announcement follows the recent groundbreaking marketing authorization of Caption Guidance software by the U.S. Food and Drug Administration (FDA). The safety and effectiveness of Caption Guidance was clinically validated in a multi-center prospective pivotal trial at Northwestern Medicine and Minneapolis Heart Institute at Allina Health with registered nurses with no prior ultrasound experience.
After years of development, machine learning methods have matured enough to be used in clinical medicine. In 2018 the FDA approved software to screen patients for diabetic retinopathy, and the methods are rapidly making their way into other applications for image analysis, natural language processing, EHR data mining, drug discovery, and more. JAMA is proud to be a primary forum for the work of interdisciplinary groups demonstrating the use of machine learning methods for clinical medicine and health care. To understand the work read JAMA's Users' Guide to the Medical Literature How to Read Articles That Use Machine Learning, authored by Google Health scientists, and an accompanying commentary. See also JAMA Network's Health Informatics collection.
Federal guidance on artificial intelligence needs additions to ensure the U.S. has a seat at the international table. The rapid proliferation of applications of artificial intelligence and machine learning--or AI, for short--coupled with the potential for significant societal impact has spurred calls around the world for new regulation. The European Union and China are developing their own rules, and the Organization for Economic Cooperation and Development has developed principles that enjoy the support of its members plus a handful of other countries. In January, the U.S. Office of Management and Budget (OMB) also issued its own draft guidance, ensuring the United States a seat at the table during this ongoing, multi-year, international conversation. The U.S. guidance--covering "weak" or narrow AI applications of the kind we experience today--reflects a light-touch approach to regulation, consistent with a desire to reward U.S. ingenuity.
This model was designed to look for chemical features that make molecules effective at killing E.coli in a process that involved training on 2,500 molecules including 1,700 FDA approved drugs and a set of 800 natural products with diverse structures and a range of bioactivities. Once trained it was tested on a library of 6,000 compounds, and the model picked out one molecule predicted to have strong antibacterial activity and chemical structure different from any existing antibiotics. Then using a different machine deep learning algorithm model the newly identified Halicin molecule was shown to likely have low toxicity to human cells.