If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Data science is a popular and lucrative profession, and despite pandemic-era slowdowns, it's still one of the sexiest jobs around. As businesses seek to employ the power of data to increasingly digital commerce, companies across industries are on the lookout for data scientists and vice versa. These data-powered professionals have a lot to offer. From manufacturing to hospitality, data scientists can bring invaluable insights that transform the ways we conduct business, leading to greater solutions and cost-reduction opportunities. While career growth may shift by industry and economic activity, the rise of data science is on an overall upward trend.
We conducted experiments to verify the robustness of our calibration procedure based on polynomial fitting. We replicated the process by taking 250 randomly picked times from the learning set. Finally, we explore the frequencies of mean and SD error as shown in Figure 15. Overall, the highest frequencies of both SBP and DBP mean error falls between 4 and 5 mmHg, which satisfies AAMI standards. Similarly, the highest frequency of SD errors is less than 8 mmHg, which also qualifies the AAMI protocol. In addition, 9 out of 35 candidates proceed 10 times of data collection to calculate the intraclass correlation coefficient (ICC). Figure 16 shows the ICC result of each candidate. The average ICC of SBP and DBP are 0.8 and 0.76, respectively.
A new AI tool that automatically measures the amount of fat around the heart from MRI scans could help predict the risk of developing diabetes and other diseases. Using the new tool, the team led by researchers from Queen Mary University of London was able to show that a larger amount of fat around the heart is associated with significantly greater chances of developing diabetes, regardless of a person's age, sex, and body mass index. The distribution of fat in the body can influence a person's risk of developing various diseases. The commonly used measure of body mass index (BMI) mostly reflects fat accumulation under the skin, rather than around the internal organs. In particular, there are suggestions that fat accumulation around the heart may be a predictor of heart disease, and has been linked to a range of conditions, including atrial fibrillation, diabetes, and coronary artery disease.
Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current machine learning techniques either depend on manually extracted features or large and complex deep learning networks which merely utilize the 1D ECG signal directly. Since intelligent multimodal fusion can perform at the stateof-the-art level with an efficient deep network, therefore, in this paper, we propose two computationally efficient multimodal fusion frameworks for ECG heart beat classification called Multimodal Image Fusion (MIF) and Multimodal Feature Fusion (MFF). At the input of these frameworks, we convert the raw ECG data into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF). In MIF, we first perform image fusion by combining three imaging modalities to create a single image modality which serves as input to the Convolutional Neural Network (CNN).
Thank you for joining us the bossy bees. I'm sitting down with Albert miles today to talk about artificial intelligence, or AI. We're excited for all the amazing capabilities this technology will bring. But we're talking about some of the insidious ways in which it can be applied. Don't forget to check out the bossy bees on Patreon for exclusive content on this podcast. Want me to go ahead? My name is Albert Myles. And I am what they call a knowledge program manager in customer content services for a large tech company, located in RTP. And that's a fancy way of saying that I am responsible for ensuring that the knowledge that's captured in support and in the development and in side of customer content is transferred to other areas effectively and efficiently. At the end of the day, I tell people, I try to help our company, learn what it already knows. And I try to help us organize what we already know. And then I help us try to distribute what all everything that we know. And it's a very, very, very new program, but I'm having fun getting it launched. And that's where we started together, and you've taken it miles and miles and miles away from where it started. And you are, I think, you know, I really dislike you putting that title on yourself, because you do so much more than, like your, your knowledge is far beyond that. And it does come together. It really does come together nicely. In your job, you know, but I think that the reason you're, you know, program has gone so far is because you bring so much experience like what we're talking about today, like you, you have such an affinity and inclination for technology that it brings a lot to the table. And then also married to something that you and I are both pretty passionate about, which is diversity, inclusion, Justice type of stuff.
We sought to investigate whether artificial intelligence (AI) and specifically deep neural networks for electrocardiogram (ECG) signal analysis can be explained using human selected features. We also sought to quantify such explainability and test if the AI model learns features that are similar to a human expert. We used a set of 100,000 ECGs that were annotated by human explainable features. We applied both linear and nonlinear models to predict published ECG AI models output for the detection of patients' age and sex. We further used canonical correlation analysis to quantify the amount of shared information between the features found in an unsupervised way.
Investigators at the Stanford University School of Medicine and the Buck Institute for Research on Aging have built an inflammatory-aging clock that's more accurate than the number of candles on your birthday cake in predicting how strong your immune system is, how soon you'll become frail or whether you have unseen cardiovascular problems that could become clinical headaches a few years down the road. In the process, the scientists fingered a bloodborne substance whose abundance may accelerate cardiovascular aging. The story of the clock's creation will be published today (July 12, 2021) in Nature Aging. "Every year, the calendar tells us we're a year older," said David Furman, PhD, the study's senior author. "But not all humans age biologically at the same rate. You see this in the clinic -- some older people are extremely disease-prone, while others are the picture of health."
The growing use of artificial intelligence in medicine is paralleled by growing concern among many policymakers, patients, and physicians about the use of black-box algorithms. In a nutshell, it's this: We don't know what these algorithms are doing or how they are doing it, and since we aren't in a position to understand them, they can't be trusted and shouldn't be relied upon. A new field of research, dubbed explainable artificial intelligence (XAI), aims to address these concerns. As we argue in Science magazine, together with our colleagues I. Glenn Cohen and Theodoros Evgeniou, this approach may not help and, in some instances, can hurt. Artificial intelligence (AI) systems, especially machine learning (ML) algorithms, are increasingly pervasive in health care.
"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To develop a convolutional neural network (CNN) solution for landmark detection in cardiac MRI. This retrospective study included cine, late-gadolinium enhancement (LGE), and T1 mapping scans from two hospitals.