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Deploying and evaluating a machine learning intervention to improve clinical care and patient outcomes is a key step in moving clinical deterioration models from byte to bedside, according to a June 13 editorial in Critical Care Medicine that comments on a Mount Sinai study published in the same issue. The main study found that hospitalized patients were 43 percent more likely to have their care escalated and significantly less likely to die if their care team received AI-generated alerts signaling adverse changes in their health.

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Aimed at undergraduate and graduate students, Argonne’s annual “Intro to AI-Driven Science on Supercomputers” training series is helping to grow a new generation of AI-ready researchers.

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UC San Diego School of Medicine part of $50M initiative to use predictive AI to help fight opioid abuse.

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Professor Wei-Ying Ma, Huiyan Chair Professor and Chief Scientist of the Institute for AI Industry Research (AIR) at Tsinghua University, delivered a HKIAS Distinguished lecture titled "Generative AI for Scientific Discovery" on 11 June 2024.

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A team of fusion researchers led by engineers at Princeton University and the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) have successfully deployed machine learning methods to suppress harmful edge instabilities — without sacrificing plasma performance. The research team demonstrated the highest fusion performance without the presence of edge bursts at two different fusion facilities — each with its own set of operating parameters.

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A research team in Korea Institute of Energy Research has successfully demonstrated the effectiveness of a green hydrogen system used to supplement the volatility of renewable energy.

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Sepsis – the leading cause of mortality in children around the world – can present with a wide range of signs and symptoms, making a one-size-fits-all treatment strategy ineffective. Pursuing a precision medicine approach for pediatric sepsis, researchers used artificial intelligence to analyze a large set of clinical data and find a distinct group of patients who might respond better to targeted treatments.

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Virtual reality technology can do more than teach weaponry skills in law enforcement and military personnel, a new study suggests: It can accurately record shooting performance and reliably track individuals’ progress over time.

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Apple and ChatGPT-maker OpenAI are now partners. With this new partnership, Apple ...

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Researchers have demonstrated a new method that could enable the large-scale manufacturing of optical qubits. The work is a major advancement that could bring us closer to a scalable quantum computer.

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Today, researchers are developing ways to accelerate discovery by combining automated experiments, artificial intelligence and high-performance computing. A novel tool developed at Oak Ridge National Laboratory that leverages those technologies has demonstrated that AI can influence materials synthesis and conduct associated experiments without human supervision.

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By using AI to assist with cancer contouring, the researchers found predicting the cancer size was 45 times more accurate and consistent than when physicians used only conventional clinical imaging and blood tests to predict the cancer extent.

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Today, researchers are developing ways to accelerate discovery by combining automated experiments, artificial intelligence and high-performance computing. A novel tool developed at Oak Ridge National Laboratory that leverages those technologies has demonstrated that AI can influence materials synthesis and conduct associated experiments without human supervision.

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Using an advanced artificial intelligence tool, researchers at the Icahn School of Medicine at Mount Sinai have identified rare coding variants in 17 genes that shed light on the molecular basis of coronary artery disease (CAD), the leading cause of morbidity and mortality worldwide. The discoveries, detailed in the June 11 online issue of Nature Genetics, reveal genetic factors impacting heart disease that open new avenues for targeted treatments and personalized approaches to cardiovascular care. The investigators used an in silico, or computer-derived, score for coronary artery disease (ISCAD) that holistically represents CAD, as described in a previous paper by the team in The Lancet. The ISCAD score incorporates hundreds of different clinical features from the electronic health record, including vital signs, laboratory test results, medications, symptoms, and diagnoses. To build the score, they trained machine learning models on the electronic health records of 604,914 individuals

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A framework based on advanced AI techniques can solve complex, computationally intensive problems faster and in a more more scalable way than state-of-the-art methods, according to a study led by engineers at the University of California San Diego. In the paper, which was published May 30 in Nature Machine Intelligence, researchers present HypOp, a framework that uses unsupervised learning and hypergraph neural networks.

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A review of existing research shows there is a growing benefit to using specialist scanning to diagnose endometriosis. Endometriosis affects 1 in 9 women and those assigned female at birth. However, it takes 6.4 years to diagnose using the conventional standard of surgical laparoscopy.

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