Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 2727

ROCHESTER, Minn. — A recent study based on real-world community patient data confirms the effectiveness of the Pooled Cohort Equation (PCE), developed by the American Heart Association and the American College of Cardiology in 2013. The PCE is used to estimate a person’s 10-year risk of developing clogged arteries, also known as atherosclerosis, and guide heart attack and stroke prevention efforts. Study findings are published in the Journal of the American College of Cardiology.

The new study highlights to patients and clinicians the continued reliability and effectiveness of the PCE as a tool for assessing cardiovascular risk, regardless of statin use to lower cholesterol.

The PCE serves as a shared decision-making tool for a clinician and patient to evaluate their current status in preventing atherosclerotic cardiovascular disease. The calculator considers input in the categories of gender, age, race, total cholesterol, HDL cholesterol, systolic blood pressure, treatment for high blood pressure, diabetes status, and smoking status.

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Advances in imaging technologies are giving physicians unprecedented insights into disease states, but fragmented and siloed information technology systems make it difficult to provide the personalized, coordinated care that patients expect.

In the field of medical imaging, health care providers began replacing radiographic films with digital images stored in a picture and archiving communication system (PACS) in the 1980s. As this wave of digitization progressed, individual departments—ranging from cardiology to pathology to nuclear medicine, orthopedics, and beyond—began acquiring their own, distinct IT solutions.

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TOKYO, Oct 4 (Reuters) — SoftBank (9984.T) CEO Masayoshi Son said he believes artificial general intelligence (AGI), artificial intelligence that surpasses human intelligence in almost all areas, will be realised within 10 years.

Speaking at the SoftBank World corporate conference, Son said he believes AGI will be ten times more intelligent than the sum total of all human intelligence. He noted the rapid progress in generative AI that he said has already exceeded human intelligence in certain areas.

“It is wrong to say that AI cannot be smarter than humans as it is created by humans,” he said. “AI is now self learning, self training, and self inferencing, just like human beings.”

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For more information on liver cancer treatment or #YaleMedicine, visit: https://www.yalemedicine.org/stories/artificial-intelligence-liver-cancer.

With liver cancer on the rise (deaths rose 25% between 2006 and 2015, according to the CDC), doctors and researchers at the Yale Cancer Center are highly focused on finding new and better treatment options. A unique collaboration between Yale Medicine physicians and researchers and biomedical engineers from Yale’s School of Engineering uses artificial intelligence (AI) to pinpoint the specific treatment approach for each patient. First doctors need to understand as much as possible about a particular patient’s cancer. To this end, medical imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) are valuable tools for early detection, accurate diagnosis, and effective treatment of liver cancer. For every patient, physicians need to interpret and analyze these images, along with a multitude of other clinical data points, to make treatment decisions likeliest to lead to a positive outcome. “There’s a lot of data that needs to be considered in terms of making a recommendation on how to manage a patient,” says Jeffrey Pollak, MD, Robert I. White, Jr. Professor of Radiology and Biomedical Imaging. “It can become quite complex.” To help, researchers are developing AI tools to help doctors tackle that vast amount of data. In this video, Julius Chaprio, MD, PhD, explains how collaboration with biomedical engineers like Lawrence Staib, PhD, facilitated the development of specialized AI algorithms that can sift through patient information, recognize important patterns, and streamline the clinical decision-making process. The ultimate goal of this research is to bridge the gap between complex clinical data and patient care. “It’s an advanced tool, just like all the others in the physician’s toolkit,” says Dr. Staib. “But this one is based on algorithms instead of a stethoscope.”

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The World Health Organization (WHO) has endorsed a second malaria vaccine to protect children against the deadly disease, which killed 619,000 people in 2021.

Researchers say that the vaccine, known as R21, is easier to make than the first-approved malaria vaccine, called RTS, S, and will be cheaper per dose.

“There’s going to be enough of it to actually give out to children,” says Jackie Cook, a malaria researcher at the London School of Hygiene and Tropical Medicine.

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Jets that develop along the walls of fluid-based thermal-energy-storage systems induce multiple flows that limit the devices’ ability to store energy.

Converting waste heat from renewable-energy technologies into electricity could reduce the need for fossil-fuel power stations—but only if that energy can be stored efficiently, for example, in a thermal battery. Researchers have partially solved this problem by designing batteries with vacuum insulation panels that reduce thermal leakage to the environment. But the useful energy available to the system can diminish even if environmental heat loss is reduced to zero. Now Christian Cierpka of the Technical University of Ilmenau, Germany, and colleagues have explored one such energy drain: mixing of hot and cold regions within a fluid-based energy-storage device [1]. The results could aid in the design of more-efficient thermal-energy-storage systems, potentially making such facilities useful as backups for intermittent renewable-energy sources.

The team studied mixing in a common thermal-energy-storage system in which a hot fluid reservoir sits atop a cold one. Between the reservoirs lies a transition layer with a temperature gradient across its width. The maximum energy output of such a battery depends on the temperature difference between the hot and cold reservoirs. Any drop in this difference will reduce the battery’s recoverable energy.

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The more physicists use artificial intelligence and machine learning, the more important it becomes for them to understand why the technology works and when it fails.

The advent of ChatGPT, Bard, and other large language models (LLM) has naturally excited everybody, including the entire physics community. There are many evolving questions for physicists about LLMs in particular and artificial intelligence (AI) in general. What do these stupendous developments in large-data technology mean for physics? How can they be incorporated in physics? What will be the role of machine learning (ML) itself in the process of physics discovery?

Before I explore the implications of those questions, I should point out there is no doubt that AI and ML will become integral parts of physics research and education. Even so, similar to the role of AI in human society, we do not know how this new and rapidly evolving technology will affect physics in the long run, just as our predecessors did not know how transistors or computers would affect physics when the technologies were being developed in the early 1950s. What we do know is that the impact of AI/ML on physics will be profound and ever evolving as the technology develops.

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