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NHS report recommends AI educational material for staff to be deployed

#artificialintelligence

The development and deployment of "educational pathways and materials" for healthcare staff on the use of AI is the main recommendation from an NHS report. The'Understanding Healthcare Workers' Confidence in AI' report is the first of two reports to be released in light of the Topol Review in 2019 which recommended the use of digital technologies such as AI and robotics to achieve digital transformation. The report, which was developed by Health Education England and NHS AI Lab, explores the confidence healthcare workers have in AI and what could drive that to help support the further implementation of AI within the NHS. It suggests that clinicians require training and education opportunities to help manage the gap between their opinion or intuition on a patient's condition and the recommendations made by AI technology. "The main recommendation of this report is therefore to develop and deploy educational pathways and materials for healthcare professionals at all career points and in all roles, to equip the workforce to confidently evaluate, adopt and use AI," the report states.


NHS Introduces A New AI-Based Technology That Can Detect Heart Disease At Record Speed And With 40 Percent Higher Accuracy

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The NHS is now employing a cutting-edge AI program that can diagnose heart illness in just 20 SECONDS. While the patient is in the scanner, the computer tool, which resembles human ability but with more precision and speed, can analyze cardiac MRI data in 20 seconds. According to the British Heart Foundation (BHF), which has supported research into the technology, this is significantly faster than a doctor physically examining the pictures following an MRI scan, which may take up to 13 minutes. The technology identifies heart structure and function changes with 40% greater accuracy and retrieves 40% more information than a human can. According to the new research, the approach was more accurate at analyzing MRIs than the work of three specialists.


NHS rolls out AI tool which detects heart disease in 20 seconds

#artificialintelligence

The NHS has rolled out a new artificial intelligence (AI) tool which can detect heart disease in just 20 seconds while patients are in an MRI scanner. A British Heart Foundation (BHF) funded study published in the Journal of Cardiovascular Magnetic Resonance concluded the machine analysis had superior precision to three clinicians. It would usually take a doctor 13 minutes or more to manually analyse images after an MRI scan has been performed. The technology is being used on more than 140 patients a week at University College London (UCL) Hospital, Barts Heart Centre at St Bartholomew's Hospital, and Royal Free Hospital. Later this year it will be introduced to a further 40 locations across the UK and globally.


UCL: AI heart disease detector begins NHS roll-out

#artificialintelligence

The first-of-its-kind AI tool, described in a new paper in the Journal of Cardiovascular Magnetic Resonance, analyses heart MRI scans in just 20 seconds whilst the patient is in the scanner. This compares to the 13 minutes or more it would take for a doctor to manually analyse the images after the MRI scan has been performed. Each year, around 120,000 heart MRI scans are performed in the UK. The researchers say that the AI will free-up valuable time of healthcare professionals – saving around 3,000 clinician days every year – so their attention can be directed to seeing more patients on NHS waiting lists, which will ultimately help with the backlog in vital heart care. The AI will also give patients and doctors more confidence in the results so that they can make better decisions about a person's treatment and possible surgeries.


Machine Learning: Algorithms, Models, and Applications

arXiv.org Artificial Intelligence

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.


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.


MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data

arXiv.org Artificial Intelligence

A major challenge in embedding or visualizing clinical patient data is the heterogeneity of variable types including continuous lab values, categorical diagnostic codes, as well as missing or incomplete data. In particular, in EHR data, some variables are {\em missing not at random (MNAR)} but deliberately not collected and thus are a source of information. For example, lab tests may be deemed necessary for some patients on the basis of suspected diagnosis, but not for others. Here we present the MURAL forest -- an unsupervised random forest for representing data with disparate variable types (e.g., categorical, continuous, MNAR). MURAL forests consist of a set of decision trees where node-splitting variables are chosen at random, such that the marginal entropy of all other variables is minimized by the split. This allows us to also split on MNAR variables and discrete variables in a way that is consistent with the continuous variables. The end goal is to learn the MURAL embedding of patients using average tree distances between those patients. These distances can be fed to nonlinear dimensionality reduction method like PHATE to derive visualizable embeddings. While such methods are ubiquitous in continuous-valued datasets (like single cell RNA-sequencing) they have not been used extensively in mixed variable data. We showcase the use of our method on one artificial and two clinical datasets. We show that using our approach, we can visualize and classify data more accurately than competing approaches. Finally, we show that MURAL can also be used to compare cohorts of patients via the recently proposed tree-sliced Wasserstein distances.


AI detects Covid 26% faster than lateral flow tests

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Artificial intelligence can enable busy NHS emergency departments to perform bedside checks for Covid-19 in just 10 minutes without the need for a laboratory, a study led by Oxford University shows. During a three-month evaluation at John Radcliffe Hospital, Oxford's main accident and emergency centre, the study found that AI test results were available 45 minutes after a patient arrived, 26% faster those for a lateral flow test. The AI screening test, known as CURIAL-Rapide, uses routine healthcare data (blood tests and vital signs) to screen patients for Covid-19. Compared to lateral flow tests, the AI test was more likely to identify Covid-19 in patients and correctly ruled out the infection 99.7% of the time, the research found. In addition, a collaboration with five NHS trusts between December 2020 and March 2021 – University Hospitals Birmingham, Portsmouth University and Bedfordshire Hospitals – the study found that the AI test performed consistently in 72,000 admissions. It provided reliable negative results for uninfected patients up to 98.8% of the time and was 21% more effective at identifying Covid-19 positive patients than lateral flow tests.


The State of AI Ethics Report (Volume 5)

arXiv.org Artificial Intelligence

This report from the Montreal AI Ethics Institute covers the most salient progress in research and reporting over the second quarter of 2021 in the field of AI ethics with a special emphasis on "Environment and AI", "Creativity and AI", and "Geopolitics and AI." The report also features an exclusive piece titled "Critical Race Quantum Computer" that applies ideas from quantum physics to explain the complexities of human characteristics and how they can and should shape our interactions with each other. The report also features special contributions on the subject of pedagogy in AI ethics, sociology and AI ethics, and organizational challenges to implementing AI ethics in practice. Given MAIEI's mission to highlight scholars from around the world working on AI ethics issues, the report also features two spotlights sharing the work of scholars operating in Singapore and Mexico helping to shape policy measures as they relate to the responsible use of technology. The report also has an extensive section covering the gamut of issues when it comes to the societal impacts of AI covering areas of bias, privacy, transparency, accountability, fairness, interpretability, disinformation, policymaking, law, regulations, and moral philosophy.


The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI

arXiv.org Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.