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Potential Liability for Physicians Using Artificial Intelligence


Artificial intelligence (AI) is quickly making inroads into medical practice, especially in forms that rely on machine learning, with a mix of hope and hype.1 Multiple AI-based products have now been approved or cleared by the US Food and Drug Administration (FDA), and health systems and hospitals are increasingly deploying AI-based systems.2 For example, medical AI can support clinical decisions, such as recommending drugs or dosages or interpreting radiological images.2 One key difference from most traditional clinical decision support software is that some medical AI may communicate results or recommendations to the care team without being able to communicate the underlying reasons for those results.3

AEYE Health and Optomed to introduce a handheld AI fundus camera


AEYE Health and Optomed have agreed to enter clinical and commercial collaboration to introduce an AI fundus camera Aurora AEYE. The collaboration includes a clinical trial with the aim to receive U.S. FDA approval for autonomous AI for retinal screening. Once the clinical trials are commenced and successfully completed, the Aurora AEYE will include Optomed's handheld fundus camera Aurora and AEYE Health's AI-based retinal screening system that aims to provide analysis of the retina for diabetic retinopathy changes and receive diagnostic results within 60 seconds. The Aurora AEYE simplifies the retinal screening process by providing an easy-to-use retinal screening system to examine these patients outside the ophthalmologist's office, including primary care and endocrinology clinics or pharmacies. Zack Dvey-Aharon, Ph.D., Co-founder and CEO of AEYE Health: "Today, we are taking a big step forward in the direction of providing accurate, affordable and useable solution to detect retinal conditions, prevent blindness and save lives. The use of our advanced AI algorithms and Optomed's quality handheld fundus cameras sets to democratize diagnostic eye screenings and ensure that all patients who need treatment will receive it on time. We are delighted to team up with Optomed and we look forward to spearheading global efforts to develop AI-based solutions for the early detection of a wide variety of retinal diseases."

Artificial intelligence in COVID-19 drug repurposing


One study estimated that pharmaceutical companies spent US$2·6 billion in 2015, up from $802 million in 2003, for the development of a new chemical entity approved by the US Food and Drug Administration (FDA). N Engl J Med. 2015; 372: 1877-1879 The increasing cost of drug development is due to the large volume of compounds to be tested in preclinical stages and the high proportion of randomised controlled trials (RCTs) that do not find clinical benefits or with toxicity issues. Given the high attrition rates, substantial costs, and low pace of de-novo drug discovery, exploiting known drugs can help improve their efficacy while minimising side-effects in clinical trials. As Nobel Prize-winning pharmacologist Sir James Black said, "The most fruitful basis for the discovery of a new drug is to start with an old drug". New uses for old drugs.

Aidoc Adds $20M To Series B For AI-based Radiology Tools


Catching abnormalities on a medical image is important, but case backlogs often mean radiologists are cut short on how long they can spend with each one. Enter Aidoc, a 4-year-old Israel-based startup providing artificial intelligence tools for radiologists. The company secured an additional $20 million for its Series B funding led by Square Peg Capital, which initially led the round that began in April 2019. The new funds bring the Series B round to $47 million and gives Aidoc a total of $60 million raised to date, according to Crunchbase data. If the AI detects something, the tools alert the radiologist, Aidoc co-founder and CEO Elad Walach told Crunchbase News. "What has happened in recent history is that scanners have become cheaper, so now there is more imaging, which is overloading a radiologist's workflow," he said.

Elon Musk is one step closer to connecting a computer to your brain


At a Friday event, Elon Musk revealed more details about his mysterious neuroscience company Neuralink and its plans to connect computers to human brains. While the development of this futuristic-sounding tech is still in its early stages, the presentation was expected to demonstrate the second version of a small, robotic device that inserts tiny electrode threads through the skull and into the brain. Musk said ahead of the event he would "show neurons firing in real-time. And he did just that. At the event, Musk showed off several pigs that had prototypes of the neural links implanted in their head, and machinery that was tracking those pigs' brain activity in real time. The billionaire also announced the Food and Drug Administration had awarded the company a breakthrough device authorization, which can help expedite research on a medical device. Like building underground car tunnels and sending private rockets to Mars, this Musk-backed endeavor is incredibly ambitious, but Neuralink ...

The Most Significant AI Policy Developments in the United States in 2019


The federal government took several important steps that prioritized AI development and deployment and positioned the United States to strengthen its global AI leadership, beginning with President Trump's "Executive Order on Maintaining American Leadership in Artificial Intelligence," which set the tone for the rest of the year. February 11: President Trump issued Executive Order 13859, "Maintaining American Leadership in Artificial Intelligence," which launched the American AI Initiative, the official U.S. national AI strategy. The initiative includes five pillars: investing in AI research and development, making federal AI resources more available, setting standards for AI, training an AI workforce, and promoting a pro-innovation international environment. The executive order stresses the importance of "continued American leadership" in AI to "maintaining the economic and national security of the United States," as President Trump wrote in a press release accompanying the order. April 2: The Food and Drug Administration (FDA) released a proposed regulatory framework for AI-based software as a medical device, including as a tool for disease detection, diagnosis, targeted therapies, or personalized medicine.

TinyML is breathing life into billions of devices


Until now building machine learning (ML) algorithms for hardware meant complex mathematical mode s based on sample data, known as " training data," in order to make predictions or decisions without being explicitly programmed to do so. And if this sounds complex and expensive to build, it is. On top of that, traditionally ML related tasks were translated to the cloud, creating latency, consuming scarce power, and putting machines at the mercy of connection speeds. Combined, these constraints made computing at the Edge slower, more expensive, and less predictable. Tiny Machine Learning (TinyML) is the latest embedded software technology that moves hardware into an almost magical realm, where machines can automatically learn and grow through use, like a primitive human brain. Granted Medicare New Technology Add-on Payment


In a groundbreaking ruling, CMS has granted the first New Technology Add-on Payment (NTAP) for artificial intelligence software. NTAP, part of the CMS Inpatient Prospective Payment System (IPPS), was set up to support the adoption of cutting-edge technologies that have demonstrated substantial clinical improvement and ensure early availability to Medicare patients. In the US, stroke is the number one cause of long term disability, but is a treatable condition if identified early enough. has been recognized by Forbes, Fast Company, and AuntMinnie as one of the leading AI healthcare companies in the US. The company provides software that improves clinical and financial outcomes1,2 by streamlining acute care, leading to shorter time to treatment, improved patient outcomes, reduced length of stay, and increased number of procedures.

Coronavirus US: Boston Dynamics' robot dog detects symptoms

Daily Mail - Science & tech

A hospital in Massachusetts has found another job for Spot, Boston Dynamics' dog-like robot: Doctor. The yellow-and-black quadruped has been proven able to take patients' vital signs from a distance of over six feet. That could allow healthcare workers to keep a safe distance from patients who may be infected with the coronavirus or other contagion. So far, Spot has only been tested on healthy patients at Harvard Medical School's Brigham and Women's Hospital - the next step would be to try it out in an emergency room setting. Researchers at MIT say they've developed cameras that allow Spot, Boston Dynamics' dog-like robot, to take vital signs from more than six feet away.

Regulation of Artificial Intelligence in Drug Discovery and Health Care


It is going to be interesting to see how society deals with artificial intelligence, but it will definitely be cool. Artificial intelligence (AI) can be defined to mean the use of intelligent machines to replicate and augment the intelligence of human beings. The Turing test was propounded to show what factors determine whether a machine operates on artificial intelligence or not. AI applications are being used in various fields such as telecommunication, banking, agriculture, manufacturing, health care, and transportation. The implementation of AI in health care aims to enhance the lives of the patients and enable physicians, doctors, hospitals, and administrators to improve health care delivery in a cost-effective and time-efficient manner. The traditional drug industry is also experiencing a wave of change due to the implementation of AI-based processes in drug discovery and development. Substitution of AI technology-based solutions in place of the traditional methods for drug discovery is expected to reduce the time for drug development. Using AI in clinical trials has reduced the time required for drug trials from 4–6 months to three months. After the analysis of the genomic data from different patients, AI helps by selecting only those patients whose genetic profile suggests it will help them to undergo testing in the clinical trial.2 Machine learning technologies, deep learning algorithms, various neural networks (such as artificial neural networks or computational neural networks), and content screening are a few examples of AI that have brought radical changes to the process of drug discovery and development.