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The future of voice AI in healthcare

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Famous social media influencer Gary Vayernurchuk says, "The future belongs to Voice." Look at all AI (artificial intelligence) driven assistants around us, from Alexa to Google Assistant, there is inherent convenience in just saying it out loud and having voice based conversations with your'virtual' assistant rather than writing commands or selecting a drop down menu. We all know that healthcare needs to be digitised in order to reach the next level of patient care. Technologies like AI and Blockchain need to be integrated into existing healthcare systems in order to make them more efficient. But these technologies can only work if all our processes are digitised first.


New NHS imaging resource assists AI in Covid fight

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Published today in the Open-Access, Open-Data journal GigaScience is the National COVID-19 Chest Imaging Database (NCCID), a centralised database containing chest X-rays, Computed Tomography (CT) and MRI scans from patients across the UK. Utilising the unique position as the world's single largest integrated healthcare system, the benefits of collecting chest imaging data this large are extensive and already being used by doctors and the research community. The database is already supporting the development of Artificial Intelligence (AI)-powered image processing software and diagnostic products and models being used to predict COVID-19 mortality in the UK. And also has the potential to become a long-term resource for teaching radiologists. These efforts provide the potential to enable faster patient assessment in Accident and Emergency, save clinicians time, and increase the safety and consistency of care across the UK. With the GigaScience paper describing how to access this Open Data resource, the NCCID training data is available to users anywhere in the world, including software developers, academics and clinicians, via a rigorous Data Access Request process.


New Artificial Intelligence projects funded to tackle health inequalities

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NHSX' NHS AI Lab and the Health Foundation have today awarded £1.4m to four projects to address racial and ethnic health inequalities using artificial intelligence (AI). The winning projects range from using AI to investigate disparities in maternal health outcomes to developing standards and guidance to ensure that datasets for training and testing AI systems are inclusive and generalisable. The NHS AI Lab introduced the AI Ethics Initiative to support research and practical interventions that complement existing efforts to validate, evaluate and regulate AI-driven technologies in health and care, with a focus on countering health inequalities. Today's announcement is the result of the Initiative's partnership with The Health Foundation on a research competition, enabled by NIHR, to understand and enable opportunities to use AI to address inequalities and to optimise datasets and improve AI development, testing and deployment. 'As we strive to ensure NHS patients are amongst the first in the world to benefit from leading AI, we also have a responsibility to ensure those technologies don't exacerbate existing health inequalities.


FDA Joins Other Regulators in Focus on AI and Machine Learning

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The Food and Drug Administration recently sought comments on the role of transparency for artificial intelligence and machine learning-enabled medical devices. The FDA invited comments in follow up to a recent workshop on the topic. The workshop was part of a series of efforts the FDA has had in this space. These include its Digital Health Center of Excellence and a five-part Action Plan for AI and machine-learning enabled medical devices. As part of the action plan, the FDA indicated it wants to issue guidance on software learning over time and help the industry be "patient-centered."


FDA Joins Other Regulators in Focus on AI and Machine Learning – National Law Review

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The Food and Drug Administration recently sought comments on the role of transparency for artificial intelligence AI and machine learning-enabled …


Accelerating healthcare AI innovation with Zero Trust technology

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From research to diagnosis to treatment, AI has the potential to improve outcomes for some treatments by 30 to 40 percent and reduce costs by up to 50 percent. Although healthcare algorithms are predicted to represent a $42.5B market by 2026, less than 35 algorithms have been approved by the FDA, and only two of those are classified as truly novel.1 Obtaining the large data sets necessary for generalizability, transparency, and reducing bias has historically been difficult and time-consuming, due in large part to regulatory restrictions enacted to protect patient data privacy. That's why the University of California, San Francisco (UCSF) collaborated with Microsoft, Fortanix, and Intel to create BeeKeeperAI. It enables secure collaboration between algorithm owners and data stewards (for example, healthy systems, etc.) in a Zero Trust environment (enabled by Azure Confidential Computing), protecting the algorithm intellectual property (IP) and the data in ways that eliminate the need to de-identify or anonymize Protected Health Information (PHI)--because the data is never visible or exposed. By uncovering powerful insights in vast amounts of information, AI and machine learning can help healthcare providers to improve care, increase efficiency, and reduce costs.


Digital technology promises to transform healthcare

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From the stethoscope to the CT scanner, technology and healthcare have long gone hand in hand. But the difficulty, especially in an age when budgets are stretched and digital innovations are proliferating, is deciding which technologies will deliver the biggest public health benefits. These need not be cutting-edge innovations. One important step is simply to replace existing analogue systems with digital ones, says David Maguire, senior analyst in the policy team at health think-tank the King's Fund. This year, Maguire co-authored a report that analysed the evidence on digital technology in health and social care. Among the most promising areas it identified was communications, both internally and when dealing with patients.


FDA Issues New Guidance For Use Of AI In Health Care

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The U.S. Food and Drug Administration recently partnered with Health Canada and the UK's Medicines and Healthcare products Regulatory Agency to issue guiding principles to align efforts and standards for artificial intelligence and machine learning medical device development in health care. "The FDA believes that artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day," said Jim McKinney, public affairs specialist at the FDA, in an email to The Well News. McKinney said the 10 guiding principles grew out of collaborative discussions with Health Canada and MHRA, and learning from several sectors that applied AI and ML technologies for years and have developed good practices that can be readily applied to the medical device industry. Evidence from published information, expert and other public perspectives and review experience was used to develop the guiding principles that will be used by the agency to lay the foundation for the development of Good Machine Learning Practice, which will unify international efforts for medical device development. Over the past decade the FDA has reviewed and authorized a growing number of devices legally marketed with machine learning and expects this trend to continue.


TD Pilot will let people with disabilities control iPads with their eyes

Engadget

There's plenty new in iPadOS 15, but it also features an under-sung accessibility upgrade: support for third-party eye-tracking devices. That'll allow people with disabilities to use iPad apps and speech generation software simply through eye movements -- no touchscreen interaction required. Tobii Dynavox, the assistive tech division of the eye-tracking company Tobii, worked with Apple for years to help make that happen. And now, the firm is ready to announce TD Pilot, a device that aims to bring the iPad experience to the estimated 50 million people globally who need communication assistance. The TD Pilot is basically a super-powered frame for Apple's tablets: It can fit in something as big as the iPad Pro 12.9-inch, and it also packs in large speakers, an extended battery and a wheelchair mount.


Automation of Data De-identification - John Snow Labs

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With evermore personal data being produced and stored by organizations, data privacy is becoming an increasing priority. Businesses have access to a lot of sensitive information about their customers, service providers, and employees and are required to protect that data in order to minimize the risks of scams or fraud. De-identification is used to overcome data privacy challenges and keep information safe from unauthorized parties. This post explains what de-identification is, how it works and how natural language processing (NLP) is used to automate the process of removing sensitive data from datasets. De-identification is a technique used to remove any data that could identify a person from a dataset.