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FDA authorizes AI-based software for prostate cancer detection


The FDA has authorized the marketing of Paige Prostate, an AI-based software platform to help pathologists identify prostate cancer when they review slide images from prostate biopsies.1 The standard biopsy review process involves the pathologist examining digitally scanned slide images from prostate biopsies to find areas that are suspicious for cancer. Paige Prostate provides a supplementary assessment of the image and locates the area with the highest probability of harboring cancer. The pathologist can then examine this specific area further if they did not identify it on their initial assessment. "Pathologists examine biopsies of tissue suspected for diseases, such as prostate cancer, every day. Identifying areas of concern on the biopsy image can help pathologists make a diagnosis that informs the appropriate treatment," Tim Stenzel, MD, PhD, director of the Office of In Vitro Diagnostics and Radiological Health in the FDA's Center for Devices and Radiological Health, stated in a press release.

Amid Skepticism, Biden Vows a New Era of Global Collaboration

The New Yorker

Joe Biden made his début at the elegant green-marble rostrum of the United Nations this week, as the coronavirus infected more than half a million people each day worldwide, as wildfires and floods aggravated by climate change ravaged the Earth, and as the U.S. struggled to prevent a new cold war with China. In lofty language, the President tried to redirect the world's focus away from the calamitous end to America's longest war, in Afghanistan, and a recent bust-up with its most longstanding ally, France. Just eight months into his Presidency, Biden is already trying to hit reset on his foreign policy. "I stand here today for the first time in twenty years with the United States not at war. We've turned the page," Biden told the chamber.

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An artificial intelligence (AI) program can spot signs of lung cancer on CT scans a year before they can be diagnosed with existing methods, according to research presented at the European Respiratory Society International Congress. Lung cancer is often diagnosed at a late stage when treatment is less likely to succeed. Researchers hope that using AI to support lung cancer screening could make the process quicker and more efficient, and ultimately help diagnose more patients at an early stage. Computerised tomography or CT scans are already used to spot signs of lung tumours, followed by a biopsy or surgery to confirm whether the tumour is malignant. But each scan involves an expert radiologist examining around 300 images and looking for signs of cancer that can be very small.

New Test Leverages Machine Learning to Diagnose and Predict Sepsis


Sepsis is a huge healthcare concern. "You take every single cancer and all the deaths due to every single cancer and you add them all up together. More people die from sepsis worldwide than that," said Bobby Reddy, Jr., CEO of Prenosis, in an interview with MD DI. And even if patients survive, they can have lifelong consequences. "Sepsis occurs when you have a very abnormal, unhealthy reaction to infection," Reddy said.

Researchers Develop AI Algorithm To Diagnose Deep Vein Thrombois


According to the Centers for Disease Control and Prevention, the number of people who die from deep vein thrombosis (DVT), ten to 30 percent of people will die within one month of diagnosis. The CDC estimates that around 60,000 to 100,000 Americans die of DVT each year. Researchers from the University of Oxford say they have developed an artificial intelligence (AI) algorithm to help diagnose DVT faster and more efficiently than a traditional radiologist-interpreted diagnostic scan. Working with researchers at the University of Sheffield and UK startup, ThinkSono, the collaborative team trained a machine learning AI algorithm called AutoDVT to differentiate patients with DVT from those who did not. The researchers believe this rapid diagnosis could reduce long patient waiting lists and unnecessary prescriptions to treat DVT when the patients do not have DVT.

Surgeons to reach prostate with robot


Prostate cancer is the most common form of cancer in men. Every year about 13,000 Dutch men are diagnosed with this disease. According to the Prostate Cancer Foundation, about 1 in 10 men suffers from prostate cancer at some point in their lives. When an all-male TU Delft student team started working with PhD researcher Martijn de Vries to design a robot that can precisely place a radiation source in your body with a steerable needle, it took a while for these statistics to sink in. But once that happened, motivation shot up.

DBS eyes AI and blockchain talent


DBS Bank plans to hire 150 developers and engineers skilled in artificial intelligence (AI) and blockchain through its Hack2Hire programme. Into its fourth edition, Hack2Hire is a virtual hackathon where participants are required to solve a range of business and technology problems that will test not only their technical competencies, but also their approach to problem solving and teamwork. Successful candidates will be invited for a final interview during the same event. Hack2Hire was last organised in 2019 and was put on hold last year due to the Covid-19 pandemic. DBS has hired around 120 people from the previous three runs of the programme.

The next healthcare revolution will have AI at its center – TechCrunch


The global pandemic has heightened our understanding and sense of importance of our own health and the fragility of healthcare systems around the world. We've all come to realize how archaic many of our health processes are, and that, if we really want to, we can move at lightning speed. This is already leading to a massive acceleration in both the investment and application of artificial intelligence in the health and medical ecosystems. Modern medicine in the 20th century benefited from unprec edented scientific breakthroughs, resulting in improvements in every as pect of healthcare. As a result, human life expectancy increased from 31 years in 1900 to 72 years in 2017.

Reports of the Association for the Advancement of Artificial Intelligence's 2020 Fall Symposium Series

Interactive AI Magazine

The Association for the Advancement of Artificial Intelligence's 2020 Fall Symposium Series was held virtually from November 11-14, 2020, and was collocated with three symposia postponed from March 2020 due to the COVID-19 Pandemic. There were five symposia in the fall program: AI for Social Good, Artificial Intelligence in Government and Public Sector, Conceptual Abstraction and Analogy in Natural and Artificial Intelligence, Physics-Guided AI to Accelerate Scientific Discovery, and Trust and Explainability in Artificial Intelligence for Human-Robot Interaction. Additionally, there were three symposia delayed from spring: AI Welcomes Systems Engineering: Towards the Science of Interdependence for Autonomous Human-Machine Teams, Deep Models and Artificial Intelligence for Defense Applications: Potentials, Theories, Practices, Tools, and Risks, and Towards Responsible AI in Surveillance, Media, and Security through Licensing. Recent developments in big data and computational power are revolutionizing several domains, opening up new opportunities and challenges. In this symposium, we highlighted two specific themes, namely humanitarian relief, and healthcare, where AI could be used for social good to achieve the United Nations (UN) sustainable development goals (SDGs) in those areas, which touch every aspect of human, social, and economic development. The talks at the symposium were focused on identifying the critical needs and pathways for responsible AI solutions to achieve SDGs, which demand holistic thinking on optimizing the trade-off between automation benefits and their potential side-effects, especially in a year that has upended societies globally due to the COVID-19 pandemic. Riding on the success of the AI for Social Good symposium that was held in Washington, DC, in November 2019, we organized the 2020 version of the symposium.

Deep learning detects heart failure with preserved ejection fraction using a baseline electrocardiogram


This study included two patient cohorts. In the derivation cohort, we included n 1884 patients who presented with exertional dyspnea or equivalent and preserved ejection fraction ( 50%) and clinical suspicion for coronary artery disease. The ECGs were divided in segments, yielding a total of 77.558 samples. We trained a convolutional neural network (CNN) to classify HFpEF and control patients according to ESC criteria. An external group of 203 volunteers in a prospective heart failure screening program served as validation cohort of the CNN.