Artificial Intelligence (AI) has been put forward as a potential solution for many of the gravest problems facing society, from the opioid crisis to poverty and famine. But although technology clearly has the potential to do a great deal of good, there's a sound business reason that tech companies often pour large amounts of resources into social projects that don't seem to align with their core business of selling software and services. This is down to the fact that tackling social issues often involves developing solutions to problems very similar to those faced by businesses. Additionally, working with governments or NGOs on building these solutions can often mean access to new datasets. Learning derived from these datasets can later be developed into products and services to offer to clients (even if the data itself isn't).
In Leones, Argentina, a drone with a special camera flies low over 150 acres of wheat. It's able to check each stalk, one-by-one, spotting the beginnings of a fungal infection that could potentially threaten this year's crop. Many food producers are struggling to manage threats to their crop like disease and pests, made worse by climate change, monocropping, and widespread pesticide use. Catching things early is key. Taranis, a company that works with farms on four continents, flies high-definition cameras above fields to provides "the eyes."
Artificial intelligence is a highly complex technology that, once implemented, requires ongoing oversight to make sure it is doing what is expected of it and ensure it is operating at optimal levels. Healthcare provider organizations using AI technologies also need to make sure they're getting the biggest bang for their buck. In other words, they need to optimize the AI so that the technologies are meeting the specific needs of their organizations. We spoke with six artificial intelligence experts, each with extensive experience in healthcare deployments, who offered comprehensive advice on how CIOs and other health IT workers can optimize their AI systems and approaches to best work for their provider organizations. Optimizing AI depends on the understanding of what AI is capable of and applying it to the right problem, said Joe Petro, chief technology officer at Nuance Communications, a vendor of AI technology for medical image interpretations.
In Leones, Argentina, a drone with a special camera flies low over 150 acres of wheat. It's able to check each stalk, one-by-one, spotting the beginnings of a fungal infection that could potentially threaten this year's crop. The flying robot is powered by computer vision: a kind of artificial intelligence being developed by start-ups around the world, and deployed by farmers looking for solutions that will help them grow food on an increasingly unpredictable planet. Many food producers are struggling to manage threats to their crop like disease and pests, made worse by climate change, monocropping, and widespread pesticide use. Catching things early is key.
Between 2013-2017, 75% of residents in 16 communities in the SEARCH Study tested annually for HIV. In this population, we evaluated three strategies for using demographic factors to predict the one-year risk of HIV seroconversion: (1) membership in 1 known "Risk Group" (e.g., young woman or HIV-infected spouse); (2) a "Model-based" risk score constructed with logistic regression; (3) a "Machine Learning" risk score constructed with the Super Learner algorithm. We hypothesized Machine Learning would identify high-risk individuals more efficiently (fewer persons targeted for a fixed sensitivity) and with higher sensitivity (for a fixed number of persons targeted) than either other approach.
Two doctors are using data analysis and predictive algorithms to stretch healthcare resources in South Africa and help millions of people live with HIV. Dr. John Sargent and Dr. Ernest Darkoh co-founded BroadReach in 2003 to make the healthcare system more efficient and treat more patients. In 2010, the two developed Vantage, a data analysis platform and recommendation engine that runs on Microsoft Azure. The initial idea was to use the platform to manage and improve the public-private partnerships that support many healthcare services in Africa. The two realized that the analytic work could also improve access to healthcare in countries where there are many more people than doctors.
CytoReason's unmatched proprietary and public data, cutting-edge machine learning technologies, unique biological models and unrivalled analysis delivers: Only by understanding what human cells are doing in response to a disease, and its treatment, can we really understand how to better impact that disease. CytoReason's mission is to simulate the cells that can stimulate discovery of: We have developed a unique machine-learning driven approach to "seeing" the cells that can make the difference in patients seeing a better life. The insights our approach generates, enable pharmaceutical and biotech companies to make the right decisions, at the right time, in the drug discovery and development programs that bring better therapies. The immune system is a cell-based system. Gene-based studies can tell us a certain amount, but not the whole story.
You probably don't know it yet, but you've got some new social media followers. Chick-fil-A recently deployed a new artificial intelligence-based system to monitor millions of social media accounts for food safety issues at its 2,400-plus locations nationwide. The company's custom algorithm tracks troublesome health mentions around its restaurants, listening in on Twitter, Facebook, and other platforms for "puke," "got sick" and similar targeted keywords that could indicate a potential outbreak of foodborne illness. It's similar to the Harvard T.H. Chan School of Public Health's recently announced partnership with Google to identify potentially unsafe restaurants and an extension of the listening rooms my clients have been telling me about that have begun to pop up at corporate headquarters all over the world, filled with employees hunting for signs of the next outbreak. It's all part of a renewed focus on food safety for corporations worried that a customer eating a shred of contaminated lettuce can put you in the hospital, and when every detail of that visit can be livestreamed.
Tuberculosis (TB) can now be identified in less than an hour thanks to a new blood test. The test procedure -- developed by The University of Queensland's Emeritus Professor Ian Riley in collaboration with researchers in Tanzania, India, Mexico and the Philippines -- is hoped to positively impact TB diagnosis in adults living in remote areas. "TB has been difficult to control because its symptoms are similar to many other diseases," Prof Riley said. "Other challenges include drug resistance to the disease and the high burden of HIV-positive cases in developing countries." Prof Riley explained that the discovery of the testing procedure came from using machine learning techniques to study three groups of adults who had a persistent cough for more than three weeks.