Since the beginning of computing, AI has always been the end target, and with modern cognitive computing models, we seem to be getting closer and closer to that goal every day. Due to the amalgamation of cognitive science and based on the fundamental principle of simulating the cycle of human thought, cognitive AI applications are expected to have far-reaching impacts not only on our private lives but also on industries such as medicine, banking and more. The benefits of cognitive technology are well and truly a step further than conventional AI systems. While the basic use case of artificial intelligence is to apply the best algorithm for solving a problem, cognitive computing tries to mimic human intelligence and logical abilities by evaluating a set of variables. The cognitive computing process uses a mixture of artificial intelligence, machine learning, neural networks, sentiment analysis, natural language processing, and contextual awareness to solve everyday problems just as human beings do.
In a usual management setting, after a person has had a heart attack or stroke, algorithmic risk models are used to calculate the risk of death for the patient. These algorithms or models utilize various factors such as age of the patient, gender, previous history, family history, ethnicity etc. Treatment of the patient is often guided by these models. A new study has shown that in many cases these models fail to predict the risks accurately. This may lead to treatment choices that are unnecessary or ineffective and even risky for the patients. The new study was published in the Digital Medicine.
A group of high school students was one of the top teams to emerge from the recent AI Tech Sprint by the Department of Veterans Affairs, delivering a web application that could help match cancer patients to clinical trials. The three students from Northern Virginia entered their work in a competition that included software companies like Oracle Healthcare and MyCancerDB. Digital consulting company Composite App took the $20,000 first place prize for its solution -- a tool for helping patients stay on track with their care plan -- but the clinical trials team got an honorable mention. The tech sprint was organized by the VA's new AI institute, and it focused on partnering with outside organizations and companies interested in applying artificial intelligence tools and techniques to VA data. The high school team's members -- Shreeja Kikkisetti, Ethan Ocasio and Neeyanth Kopparapu -- met as part of the Northern Virginia-based nonprofit Girls Computing League.
No longer relegated to the ranks of science fiction, AI is rapidly becoming ubiquitous as one the most dynamic new fields in technology. But just as it cannot be consigned to fiction, AI cannot be reducible to any particular tech category, as it demonstrates applicability in an increasing array of different industries. Its core technologies - such as machine learning, deep learning, natural language processing (NLP) and computer vision - have enabled AI to penetrate and become indispensable in everything from autonomous vehicles, virtual assistants, energy, voice and text translation, retail, healthcare and more. And this is all happening fast. A report from Grand View Research, for instance, projects a compound annual growth rate (CAGR) for the global AI market of 46.2 percent from 2019 to 2025.
Leading to 2020 several European countries have launched their national AI strategies, the European Commission put forward a European approach to Artificial Intelligence, a Coordinated Plan on Artificial Intelligence "Made in Europe", laid out the path for Building Trust in Human Centric Artificial Intelligence and set up a High-Level Expert Group on Artificial Intelligence (AI HLEG) which presented their Policy and Investment Recommendations for Trustworthy AI. And in 2020, Artificial Intelligence will continue to be very much a priority for the European Union. The recent paper Artificial Intelligence: Power for Civilisation – and for Better Healthcare that I had the honor to co-author asserts that Europe's goal should be to integrate AI into health-related operations, so as to improve clinical care, drive new therapies and treatments, and make healthcare systems more efficient. Data Front and Centre Europe is currently proving that it is capable of working together and sharing as the enthusiasm resulting from what was originally named the MEGA initiative (standing for Million European Genomes Alliance and proposed by our colleagues at the European Alliance for Personalised Medicine), now renamed as European '1 Million Genomes' Initiative, has clearly demonstrated. There is an undoubted willingness on the part of many Member States, and the regions within them, to collaborate when it comes to data sharing in healthcare, and not just in genomics.
As an aspiring data scientist, the best way for you to increase your skill level is by practicing. And what better way is there for practicing your technical skills than making projects. Personal projects are a really important part of your career's growth. They will take you one step closer to your data science dream. Projects will boost your knowledge, skills, and confidence.
A technology company uses artificial intelligence to assist in cancer drug development has launched a study that will collect data on up to 1,000 blood cancer patients over the course of a year. San Francisco-based Notable said Wednesday it had launched the study, titled ANSWer, which will collect de-identified specimens with matched clinical data from participants in U.S. and Canadian clinical networks, at the time of their entry into the study and during subsequent visits. Patients with acute myeloid leukemia, acute lymphoblastic leukemia, chronic myelogenous leukemia, multiple myeloma, lymphomas, myeloproliferative disorders and others will be included. The goal is to establish a tumor registry with annotated clinical outcomes. "The observational clinical trial that we're kicking off will give us the opportunity to test more patients than ever before, allowing us to continue increasing the platform's predictive value," Notable CEO Matt De Silva said in a statement.
On a chilly evening last fall, I stared into nothingness out of the floor-to-ceiling windows in my office on the outskirts of Harvard's campus. As a purplish-red sun set, I sat brooding over my dataset on rat brains. I thought of the cold windowless rooms in downtown Boston, home to Harvard's high-performance computing center, where computer servers were holding on to a precious 48 terabytes of my data. I have recorded the 13 trillion numbers in this dataset as part of my Ph.D. experiments, asking how the visual parts of the rat brain respond to movement. Printed on paper, the dataset would fill 116 billion pages, double-spaced. When I recently finished writing the story of my data, the magnum opus fit on fewer than two dozen printed pages. Performing the experiments turned out to be the easy part. I had spent the last year agonizing over the data, observing and asking questions. The answers left out large chunks that did not pertain to the questions, like a map leaves out irrelevant details of a territory.
The current wave of emerging digital technologies offers great opportunities to transform pharma operating models and improve the declining ROI on R&D productivity. Harnessing the power of digital technologies – such as robotic process automation, artificial intelligence, machine learning and organ-on-a-chip – can transform how clinical trials are conceived, designed and conducted. For instance, they can be used to automate processes, make efficient use of Big Data and support early decision-making with predictive analytics. The beginning of this digital transformation is well underway and is likely to accelerate. Therefore, harnessing these technologies will require a deep understanding of how they work, the role they will play in advancing clinical development and the limitations they present.
OnQ, an industry-leading, healthcare RCM solutions company, is pleased to announce the successful launch of its Robotic Process Automation (RPA) products and services. OnQ's scalable RPA solutions significantly improve quality, create efficiencies, and simplify processes in medical billing. OnQ is leading the way in moving the industry forward by developing and deploying its RPA technology. "We've broken new ground in applying robotic technology to the healthcare billing industry," explains OnQ CEO Jack McBride. "OnQ's scalable RPA is an exciting development that's providing substantial benefits for both our clients and our employees. By automating repetitive tasks, we have greatly increased efficiency and freed up employees for more meaningful work. Our unique position enables us to rapidly develop, deploy, and scale RPA solutions that produce better results at a lower cost than outsourced labor."