A probabilistic artificial intelligence computer model developed at Los Alamos National Laboratory provided the most accurate state, national, and regional forecasts of the flu in 2018, beating 23 other teams in the Centers for Disease Control and Prevention's FluSight Challenge. The CDC announced the results last week. "Accurately forecasting diseases is similar to weather forecasting in that you need to feed computer models large amounts of data so they can'learn' trends," said Dave Osthus, a statistician at Los Alamos and developer of the computer model, Dante. "But it's very different because disease spread depends on daily choices humans make in their behavior--such as travel, hand-washing, riding public transportation, interacting with the healthcare system, among other things. Those are very difficult to predict."
Deep learning (DL) neural networks have only recently been employed to interpret chest radiography (CXR) to screen and triage people for pulmonary tuberculosis (TB). No published studies have compared multiple DL systems and populations. We conducted a retrospective evaluation of three DL systems (CAD4TB, Lunit INSIGHT, and qXR) for detecting TB-associated abnormalities in chest radiographs from outpatients in Nepal and Cameroon. All 1196 individuals received a Xpert MTB/RIF assay and a CXR read by two groups of radiologists and the DL systems. Xpert was used as the reference standard.
Artificial intelligence is developing at a fast pace. Numerous industries are bound to be transformed and improved by it in the years to come. We can already see examples in almost every industry out there, where AI has changed things from the ground up. Namely, one of the industries that will be impacted the most is healthcare. Healthcare is becoming better and more accessible to everyone in the world, but it could do with some help from the side.
This article started out as an addendum to a chapter in our book, Data Visualization: A History of Visual Thinking and Graphic Communication (Friendly & Wainer, 2020). In this we claimed that much of the history of data visualization could be seen as combination of three forces: (1) important scientific problems of the day, (2) a developing abundance of data, and (3) the cognitive ability of some heroes in this history to conceive solutions to problems by visual imagination. In the book and what follows we make frequent reference to cognitive aspects of the visual understanding of phenomena and their expression in graphic displays: "inner vision", "graphic communication", "visual insight" are some of the terms we use. An early metaphor for this and an early title for our book was "A gleam in the mind's eye." We give some additional explanations and examples here. We also want to place this topic in a wider framework.
James Collins, Ph.D. is on a mission to end antibiotic resistance with the help of artificial intelligence and machine learning. James Collins, the Termeer Professor of Bioengineering in the Department of Biological Engineering and Institute for Medical Engineering & Science at the Massachusetts Institute of Technology (MIT), has founded several companies based on research in synthetic biology. According to Collins, "the generic principles that apply to physical systems don't extend so well to living organic systems." Collins explains that synthetic biology starts by looking at living systems from an engineering perspective. Rather than simply understanding how everything works, synthetic biologists must determine whether it's possible to reverse-engineer cells to create desirable outcomes.
Yale University researchers have developed a way to leverage neural networks to reveal patterns of activity of individual cells from multiple individuals. Researchers at Yale University have developed a method of leveraging artificial intelligence (AI) neural networks to reveal larger patterns of activity of individual cells that come from several individuals. The AI neural network, called SAUCIE (Sparse Autoencoder for Clustering, Imputation, and Embedding), can reveal minute cellular differences within individuals, as well as broader patterns that describe how the body functions. The new method will allow researchers to identify larger clusters of cellular activity that could shed light on the basis of a host's pathogens. For example, the team used SAUCIE to analyze 20 million cells from 60 patients and identify rare Gamma-Delta T cell types that regulate how the body responds to the virus that causes Dengue fever.
It is undeniable that our lives have been made better by artificial intelligence (AI). AI technology allow us to get almost anything, anytime, anywhere in the world at the click of a button; prevent disease epidemics and keep them from spiralling out of control, and generally just make day-to-day life a bit easier by helping us to save energy, book a babysitter, manage our cash and our health all at a very low cost. AI's penetration into systems and processes in virtually all sectors of business and life has been rapid and global. The speed and scale at which AI is proliferating does however raise the question of how at-risk we may be that the AI we are building for good can also be introducing damaging bias at scale. In this two-part series, I explore the issues with AI constructs, the good bad and the ugly and how we can think about shaping a future through AI in financial services that helps lift people up rather than scaling problems up.
Antimicrobial resistance (AMR) is the ability of microorganisms like bacteria, viruses, fungi and certain parasites to resist drugs such as antibiotics, antifungals, and antivirals from destroying it. AMR is a worldwide public health threat that is projected to rise. Globally, by 2050, over 10 million deaths per year will be due to antimicrobial resistance according to projections from a report by Wellcome Trust and the UK government. For antibiotic resistance alone, each year over two million people in the U.S. are affected, and 23,000 die, according to figures from the U.S. Centers for Disease Control and Prevention (CDC). Researchers at Washington State University have combined game theory with artificial intelligence (AI) to create a tool that can identify genes that are antibiotic-resistant in bacteria, and published their study in Scientific Reports on October 9, 2019.
Fox News Flash top headlines for Oct. 14 are here. Check out what's clicking on Foxnews.com The U.S. continues to see a rise in the number of sexually transmitted diseases, according to health officials -- and in Hawaii, the increase is believed to be linked to online dating. Health officials in the Aloha State have reported a significant increase in chlamydia, gonorrhea and syphilis. All three of the infections were at or near their highest rates in about 30 years.
Recent advancements in the field of computer vision (CV) have led to new applications that could benefit people globally, and especially those in developing countries. To bring the CV community closer to tasks, data sets, and applications that can have a global impact, Facebook AI launched the Computer Vision for Global Challenges (CV4GC) initiative earlier this year. Through a series of academic programs, mentorships, sponsorships, and events, CV4GC brings together field experts from around the world to discuss potential CV applications to address issues that affect developing regions. One such program is the CV4GC request for proposals, a research award opportunity that launched in February with the goal of supporting research that aligns with CV4GC's mission. We were particularly interested in proposals that extended CV technology to achieve global development priorities, especially those captured in the United Nations' Sustainable Development Goals.