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Fitbit Sense review: A half-baked smartwatch for the wellness warrior

Mashable

It's taken almost four years, but it feels like Fitbit has finally found its footing in the world of smartwatches and the Fitbit Sense is proof -- sort of. At this point, it's no secret that Fitbit is extremely capable of manufacturing accurate, easy-to-use, sleek, and affordable fitness trackers. But when it comes to smartwatches, it's safe to say the journey hasn't been as smooth. Between 2016 and 2017, Fitbit released two devices that straddled the line between smartwatch and fitness tracker: the Blaze and Ionic. While both packed every sensor necessary to track your daily fitness needs, each one was just as clunky and unattractive as the one before it. These just weren't wrist-worn accessories anyone really wanted to wear on a daily basis.


Machine Learning Biases Might Define Minority Health Outcomes

#artificialintelligence

Whether or not you're aware, your Google searches, questions posed to Siri, and Facebook timeline all rely on artificial intelligence (AI) to perform effectively. Artificial intelligence is the simulation of human intelligence processes by machines. The goal of artificial intelligence is to build models that can perform specific tasks as intelligently as humans can, if not better. Much of the AI you encounter on a daily basis uses a technique known as machine learning, which uses predictive modeling to generate accurate predictions when given random quantities of data. Because predictive models are built to find relational patterns in data, they learn to favor efficiency over fairness.


Machine learning in Medical field

#artificialintelligence

In this post, I will test the effectiveness of machine learning in the medical field especially in classifying whether or not a person has heart disease. AS well, I will guide you through building some classifiers from the scikit-learn library then, we will highlight the best accuracy. According to the World health organization, 17.9 million deaths caused by Cardiovascular diseases each year. Our objective here is help doctors diagnose heart disease faster, and also inform patient who are at high risk. Through this post we will try to solve these important questions: 1- Who are more likely to have heart disease?


Can Machine Learning Make Fecal Testing Part of CVD Screening?

#artificialintelligence

Machine learning analysis of stool samples may provide a helpful first pass for the mass screening for any type of cardiovascular disease (CVD) in patients, researchers claimed. Various machine learning algorithms were fed gut microbiota data and, with training, were subsequently able to distinguish between people with and without CVD, with ROC curves as high as 0.70, reported a group led by Sachin Aryal, an MS student in bioinformatics at the University of Toledo, Ohio. "While this demonstrates the promising potential of applying microbiome-based ML [machine learning] for predicting CVD, in the future, it will be of interest to further calibrate and improve predictive capability of ML modeling by including more samples from different sources or stratifying specific types of CVD incorporated with combinatorial features such as health records, in addition to gut microbiome data," the authors said. Their study was presented as a poster at the virtual Hypertension meeting, sponsored by the American Heart Association, and was simultaneously published online in the November 2020 issue of Hypertension. Investigators claimed theirs as the first study to apply existing knowledge of dysbiosis of gut microbiota in CVD patients to a machine learning approach to CVD screening.


AI standards launched to help tackle problem of overhyped studies

#artificialintelligence

The first international standards for the design and reporting of clinical trials involving artificial intelligence have been announced in a move experts hope will tackle the issue of overhyped studies and prevent harm to patients. While the possibility that AI could revolutionise healthcare has fuelled excitement, in particular around screening and diagnosis, researchers have previously warned that the field is strewn with poor-quality research. Now an international team of experts has launched a set of guidelines under which clinical trials involving AI will be expected to meet a stringent checklist of criteria before being published in top journals. The new standards are being simultaneously published in the BMJ, Nature Medicine and Lancet Digital Health, expanding on existing standards for clinical trials – put in place more than a decade ago for drugs, diagnostic tests, and other interventions – to make them more suitable for AI-based systems. Prof Alastair Denniston of the University of Birmingham, an expert in the use of AI in healthcare and member of the team, said the guidelines were crucial to making sure AI systems were safe and effective for use in healthcare settings.


AI tech use by NHS to be sped up with £50m investment

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NHS patients will benefit from new artificial intelligence (AI) technologies thanks to a £50 million boost. A range of AI-powered innovations which can analyse breast cancer screening scans and assess emergency stroke patients will be tested and scaled. Take-home technology could also see patients given devices and software that can turn their smartphone into a clinical grade medical device for monitoring kidney disease, or a wearable patch to detect irregular heartbeats, one of the leading causes of strokes and heart attacks. The award is managed by the Accelerated Access Collaborative in partnership with NHSX and the National Institute for Health Research. The package also includes funding to support the research, development and testing of promising ideas which could be used in the NHS in future to help speed up diagnosis or improve care for a range of conditions including sepsis, cancer and Parkinson's.


AI standards launched to help tackle problem of overhyped studies

The Guardian

The first international standards for the design and reporting of clinical trials involving artificial intelligence have been announced in a move experts hope will tackle the issue of overhyped studies and prevent harm to patients. While the possibility that AI could revolutionise healthcare has fuelled excitement, in particular around screening and diagnosis, researchers have previously warned that the field is strewn with poor-quality research. Now an international team of experts has launched a set of guidelines under which clinical trials involving AI will be expected to meet a stringent checklist of criteria before being published in top journals. The new standards are being simultaneously published in the BMJ, Nature Medicine and Lancet Digital Health, expanding on existing standards for clinical trials – put in place more than a decade ago for drugs, diagnostic tests, and other interventions – to make them more suitable for AI-based systems. Prof Alastair Denniston of the University of Birmingham, an expert in the use of AI in healthcare and member of the team, said the guidelines were crucial to making sure AI systems were safe and effective for use in healthcare settings.


Artificial intelligence in health care: preparing for the fifth Industrial Revolution

#artificialintelligence

AI has arrived, with the potential for enormous change in the delivery of health care, but are we ready? Artificial intelligence (AI) is the trigger for the next great transformation of society: the fifth Industrial Revolution. AI has already arrived in health care, but are we ready for the kind of changes that it will introduce? In this article, we map out the current areas where AI has begun to permeate and make predictions about the kind of changes it will make to health care. AI comprises any digital system "that mimics human reasoning capabilities, including pattern recognition, abstract reasoning and planning".1 It includes the concept of machine learning, where machines are able to learn from experience in ways that mimic human behaviour, but with the ability to assimilate much more data and with potential for greater accuracy and speed.


Artificial intelligence in health care is already here, but where to next?

#artificialintelligence

Artificial intelligence (AI) in health care has arrived, with enormous potential for change in the delivery of care, but experts published in the Medical Journal of Australia today are asking if we are ready. "AI, machine learning, and deep neural network tools can assist medical decision making and management, and have already permeated into at least three different levels: AI-assisted image interpretation; AI-assisted diagnosis; and, AI-assisted prediction and prognostication," wrote the authors, Joseph Sung, the Mok Hing Yiu Professor of Medicine at the Chinese University of Hong Kong, Cameron Stewart, Professor of Health, Law and Ethics at the University of Sydney, and Professor Ben Freedman, the Deputy Director of Research Strategy at the Heart Research Institute and the University of Sydney's Charles Perkins Center and Concord Clinical School. "From diagnosing retinopathy to cardiac arrhythmias, from screening for skin cancer to breast cancer, from predicting outcome of stroke to self-management of chronic diseases, AI and machine learning devices can replace many time-consuming, labor-intensive, repetitive and mundane tasks of clinicians and give possible suggestions of management plans," Sung and colleagues wrote. The quality of AI in health care is dependent on the quality of the data on which it is based. "Algorithms are being developed and validated on data generated by health care systems where current practices may already be inequitable," they wrote.


Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist

#artificialintelligence

Machine learning (ML) has been increasingly used within cardiology, particularly in the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML algorithms, inconsistencies in the model performance and interpretation may occur. Several review articles have been recently published that introduce the fundamental principles and clinical application of ML for cardiologists. This paper builds on these introductory principles and outlines a more comprehensive list of crucial responsibilities that need to be completed when developing ML models. This paper aims to serve as a scientific foundation to aid investigators, data scientists, authors, editors, and reviewers involved in machine learning research with the intent of uniform reporting of ML investigations. An independent multidisciplinary panel of ML experts, clinicians, and statisticians worked together to review the theoretical rationale underlying 7 sets of requirements that may reduce algorithmic errors and biases. Finally, the paper summarizes a list of reporting items as an itemized checklist that highlights steps for ensuring correct application of ML models and the consistent reporting of model specifications and results. It is expected that the rapid pace of research and development and the increased availability of real-world evidence may require periodic updates to the checklist.