Artificial intelligence is already improving many aspects of our lives, including how we drive, how we socialize, and what we buy. It also has the potential to transform healthcare in variety of ways, but the biggest impact might surprise you. It would be overly simplistic to say there's only one way that AI and related technologies, like deep learning and streaming analytics, will be used in healthcare. Obviously, a technology as powerful as AI will be used in multiple ways in healthcare, a huge industry that accounts for tens of trillions of dollars in annual spending around the world, and touches nearly every one of us. Diversity has been the rule with AI and big data analytics in healthcare up to this point.
Patients have had heart failure (HF) for centuries, and it is estimated that more than 37 million people worldwide are currently affected.1 Despite being a complex clinical syndrome, contemporary clinical descriptors lag far behind its nuanced scientific understanding. In fact, current classifications used clinically and in trials rely heavily on incomplete descriptors such as left ventricular ejection fraction (LVEF) cut points, stratifying patients simply as those with "reduced" or "preserved" LVEF: HFrEF and HFpEF.2 There is increasing recognition that such classifications are discordant with the current understanding of HF and may impair our ability to personalize risk assessment and treatment. The emphasis on LVEF is particularly notable as prior studies have shown only modest differences in long‐term survival among patients with "reduced" as compared with "preserved" LVEF.3, 4 Still further, numerous promising therapies have failed to demonstrate benefit in clinical trials where inclusion was based almost exclusively on LVEF.5 Despite this, recent guidelines have recommended even further subclassification of HF according to LVEF, with the introduction of HF with "midrange ejection fraction" as a distinct clinical entity.6
Experienced lawyers in the US have been left behind by AI when it came to reviewing legal documents according to a new report – with the lawyers exhibiting 85% average accuracy compared to 94% average accuracy rate achieved by AI software. This revelation is based on a study carried out by professors at Duke Law, University of Southern California, and Stanford Law School. Metaphorically, the study was a race between LawGeex, an AI contract review platform provider, and a team of 20 top corporate lawyers with notable experience particularly in reviewing Non-Disclosure Agreements (NDAs). For the study, the lawyers and the LawGeex AI had to analyse five previously unseen contracts with 153 paragraphs of technical legal language, under controlled conditions precisely prepared the way lawyers review and approve everyday contracts. The highest performing lawyer stood in line with LawGeex AI by achieving 94% accuracy but the average accuracy achieved by the least performing lawyer stood at just 67%.
Heart disease is the leading cause of death for both men and women, according to the Centers for Disease Control and Prevention (CDC). In the U.S., one in every four deaths is a result of heart disease, which includes a range of conditions from arrhythmias, or abnormal heart rhythms, to defects, as well as blood vessel diseases, more commonly known as cardiovascular diseases. Predicting and monitoring cardiovascular disease is often expensive and tenuous, involving high-tech equipment and intrusive procedures. However, a new method developed by researchers at USC Viterbi School of Engineering offers a better way. By coupling a machine learning model with a patient's pulse data, they are able to measure a key risk factor for cardiovascular diseases and arterial stiffness, using just a smart phone.
NEW YORK--(BUSINESS WIRE)--With ransomware and distributed denial of service (DDoS) attacks on the rise, the average number of focused cyberattacks per organization has more than doubled this year compared to the previous 12 months (232 through January 2018 versus 106 through January 2017). In the face of these growing cyber threats, organizations are demonstrating far more success in detecting and blocking them, according to a new study from Accenture (NYSE:ACN). Yet, despite making significant progress, only two out of five organizations are currently investing in breakthrough technologies like machine learning, artificial intelligence (AI) and automation, indicating there is even more ground to be gained by increasing investment in cyber resilient innovations and solutions. The study was conducted from January to mid-March 2018 and investigated focused attacks defined as having the potential to both penetrate network defenses and cause damage, or extract high-value assets and processes from within organizations. Despite the increased pressure of ransomware attacks, which more than doubled in frequency last year, the study found organizations are upping their game and now preventing 87 percent of all focused attacks compared to 70 percent in 2017.
Artificial intelligence can help pharmaceutical companies to leverage improved data insights in many ways. These include reviewing and interpreting comprehensive datasets; running speedier development cycles; interpreting data in context; and other types of business intelligence. Most significantly, machine intelligence seems to be able to solve the problems that have perpetually caused road blocks with pharmaceutical development, notably time for drug discovery and the subsequent clinical trial success rate. These ideas are explored by Gunjan Bhardwaj is the founder and CEO of Innoplexus, writing for PharmaPhorum. He makes the point that, in terms of improved scrutiny of datasets, platforms that work more like "a window into the world of available information" as opposed to "high-priced collection of limited data", are required.
Both major pharmaceutical companies and startups are applying artificial intelligence in drug discovery. Often the bigger players are linking up and forming partnerships with AI startups. The drug discovery process is lengthy. Typically, it can take five years from a proposal, based on laboratory experiments, to see a new medicine hitting the market. The major gateposts are experimenting with different active ingredients; running clinical trials; and seeking regulatory approval from global regulatory, like the U.S. Food and Drug Administration.
Artificial intelligence (AI) technology, combined with automatically collected big data hold the potential to solve many key clinical trial challenges. These include increasing trial efficiency through better protocol design, patient enrollment and retention, and study start-up, which were each named as prime candidates for improvement by nearly 40% of sponsors in a recent ICON-Pharma Intelligence survey.1 With clinical trials accounting for 40% of pharma research budgets2, sponsors need new ways to accelerate timelines and reduce costs. Data-driven protocols and strategies powered by advanced AI algorithms processing data automatically collected from mobile sensors and apps, electronic medical and administrative records, and other sources have the potential to significantly cut trial costs. They achieve this by improving data quality, increasing patient compliance and retention, and identifying treatment efficacy more efficiently and reliably than ever before.
If you ask Alexa what chemtrails are, you might be surprised by what she says. The voice assistant has been spouting a government conspiracy theory as an explanation for the oft-debated condensation trails. Alexa has been recorded telling users: 'Chemtrails are trails left by aircraft [that] are actually chemical or biological agents deliberately sprayed at high altitudes for a purpose undisclosed to the general public in clandestine programs directed by government officials'. Amazon says it has taken steps to fix the issue since the error was first discovered by Mashable. Amazon's Alexa voice assistant has been telling users that chemtrails are part of a government-issued conspiracy theory.