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Patients recognize role of artificial intelligence in diagnosis, treatment

#artificialintelligence

With artificial intelligence making its way into daily life, healthcare, including ophthalmology, is no exception. Ophthalmology, with its heavy reliance on imaging, is an innovator in the field of AI in medicine. Although the opportunities for patients and health care professionals are great, hurdles to fully integrating AI remain, including economic, ethical, and data-privacy issues. "AI is impacting health care at every level, from the provider to the payer to pharma," according to Dan Riskin, MD, CEO and founder of Verantos, a health care data company in Palo Alto, California, that uses AI to sort through real world evidence. The question remains, just how to patients feel about the use of AI in the diagnosis and treatment of their illnesses? In a patient survey conducted in December 2019, 66% of respondents said AI plays a large role in their diagnosis and treatment and thought it was important.


Spotting Heart disease with AI - How far are we?

#artificialintelligence

Cardiovascular Disease has long been the number one cause of death in the U.S. and some of the stats are startling: an American will have a heart attack approximately every 40 seconds for a total of 805,000 every year, At the same time, mortality and morbidity rates of CVD are increasing year by year, especially in developing regions. Studies have shown that approximately 80% of CVD-related deaths occur in low- and middle-income countries. Besides, these deaths occur at a younger age than in high-income countries. CVD represents a significant economic cost for society, around $351.2 billion in the US, chronically affecting patients' quality of life. The EU has estimated that the overall yearly cost amounts to €210 billion, allocating around 53% to healthcare costs (€111 billion), with 26% related to productivity losses (€54 billion), and the remaining 21% (€45 billion) to the informal care of people with CVD (European Cardiovascular Disease Statistics 2017).


25 Industries & Technologies That Will Shape The Post-Virus World

#artificialintelligence

In industries from healthcare to education to finance to manufacturing, quarantine and extended work-from-home forced companies to use technology to reimagine nearly every facet of their operations. As the world reopens in fits and starts, we analyze the industries poised to thrive in a post-Covid world. As the Covid-19 pandemic has charted its unprecedented path around the world, it's carried with it the question: What will Covid-19's legacy be? From healthcare to education to entertainment to manufacturing, technology innovators are stepping forward to help answer that question. "Crisis can be… a catalyst or can speed up changes that are on the way -- it almost can serve as an accelerant." In the wake of the outbreak, everything from doctors appointments to schooling to workouts went online. As more people have worked, learned, banked, exercised, relaxed, and even sought medical care from home during Covid-19, they have gotten a crash course in just how much can be accomplished at ...


FedMed-GAN: Federated Multi-Modal Unsupervised Brain Image Synthesis

arXiv.org Artificial Intelligence

Utilizing the paired multi-modal neuroimaging data has been proved to be effective to investigate human cognitive activities and certain pathologies. However, it is not practical to obtain the full set of paired neuroimaging data centrally since the collection faces several constraints, e.g., high examination costs, long acquisition time, and even image corruption. In addition, most of the paired neuroimaging data are dispersed into different medical institutions and cannot group together for centralized training considering the privacy issues. Under the circumstance, there is a clear need to launch federated learning and facilitate the integration of other unpaired data from different hospitals or data owners. In this paper, we build up a new benchmark for federated multi-modal unsupervised brain image synthesis (termed as FedMed-GAN) to bridge the gap between federated learning and medical GAN. Moreover, based on the similarity of edge information across multi-modal neuroimaging data, we propose a novel edge loss to solve the generative mode collapse issue of FedMed-GAN and mitigate the performance drop resulting from differential privacy. Compared with the state-of-the-art method shown in our built benchmark, our novel edge loss could significantly speed up the generator convergence rate without sacrificing performance under different unpaired data distribution settings.


US taxpayers will have to submit a video selfie to access their IRS accounts

Daily Mail - Science & tech

The selfie is taken on a mobile device and then uploaded ID.me, a third-party identity verification company that will use its own facial recognition to verify the individual US taxpayers will have to submit a video selfie to access certain Internal Revenue Service (IRS) tools and applications starting this summer. The selfie is taken on a mobile device and then uploaded to ID.me, a third-party identity verification company that will use its own facial recognition to verify the individual. Once verified, the taxpayer will be asked to upload their government ID and copies of bills. Users can access basic information on the IRS without logging into ID.me, but the unique sign in will be required to make and view payments, access tax records, view or create payment plans, manage communications preference or view tax professional authorizations. However, this process is not a requirement to file taxes.


Talk to me: How AI can diagnose disease

#artificialintelligence

EXPRESSING A DISEASE: Want to know whether you have Covid-19 or even Alzheimer's? Artificial intelligence might soon have an answer just by listening to your voice. Leading researchers are developing technology that sorts through evidence of so-called vocal biomarkers to hone in on medical conditions that might not be detectable during routine office visits or exams. "This line might seem to have been lifted from a Star Trek script," said Bertalan Meskó, director of the Medical Futurist Institute. "But we are close to having such conversations with our computers."


Artificial intelligence and on-the-job safety - eMaxx Assurance Group of Companies

#artificialintelligence

Artificial intelligence already is part of our everyday lives: in our web searches, in our interactions with digital assistants, and even helping us decide what movies and TV shows to watch. In the world of worker safety, AI is providing "great opportunities." "Not only will it be in the fabric of the future of work, but it's going to be in the fabric of solutions to the future of work as well," Vietas said during a webinar hosted by the agency in June. Some of the benefits AI is providing to the safety field: deeper insights, continuous observations and real-time alerts to help employees avoid unsafe situations and organizations respond to incidents quicker. Experts say making use of AI requires collaborative efforts between safety professionals and other departments, namely information technology, to ensure transparency as well as alleviate privacy concerns and other issues workers may have.


Forecasting: theory and practice

arXiv.org Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.


Artificial intelligence and on-the-job safety

#artificialintelligence

Artificial intelligence already is part of our everyday lives: in our web searches, in our interactions with digital assistants, and even helping us decide what movies and TV shows to watch. "Not only will it be in the fabric of the future of work, but it's going to be in the fabric of solutions to the future of work as well," Vietas said during a webinar hosted by the agency in June. Some of the benefits AI is providing to the safety field: deeper insights, continuous observations and real-time alerts to help employees avoid unsafe situations and organizations respond to incidents quicker. Experts say making use of AI requires collaborative efforts between safety professionals and other departments, namely information technology, to ensure transparency as well as alleviate privacy concerns and other issues workers may have. "Our recommendation is, basically, try to understand AI and try to see how it can work for you," said Houshang Darabi, a professor at the University of Illinois Chicago and co-director of the occupational safety program at the school's Great Lakes Center for Occupational Health and Safety.


Trustworthy AI: From Principles to Practices

arXiv.org Artificial Intelligence

Fast developing artificial intelligence (AI) technology has enabled various applied systems deployed in the real world, impacting people's everyday lives. However, many current AI systems were found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection, etc., which not only degrades user experience but erodes the society's trust in all AI systems. In this review, we strive to provide AI practitioners a comprehensive guide towards building trustworthy AI systems. We first introduce the theoretical framework of important aspects of AI trustworthiness, including robustness, generalization, explainability, transparency, reproducibility, fairness, privacy preservation, alignment with human values, and accountability. We then survey leading approaches in these aspects in the industry. To unify the current fragmented approaches towards trustworthy AI, we propose a systematic approach that considers the entire lifecycle of AI systems, ranging from data acquisition to model development, to development and deployment, finally to continuous monitoring and governance. In this framework, we offer concrete action items to practitioners and societal stakeholders (e.g., researchers and regulators) to improve AI trustworthiness. Finally, we identify key opportunities and challenges in the future development of trustworthy AI systems, where we identify the need for paradigm shift towards comprehensive trustworthy AI systems.