Scientists at Washington University School of Medicine in St. Louis trained a machine-learning algorithm to predict accurately brain metastasis using biopsy samples from early-stage non-small cell lung cancer patients. It was also highly accurate in predicting those patients who do not develop metastatic brain tumors.
Digital Science is delighted to announce the launch of AI-driven summarization in Dimensions, a new feature to support the user in their discovery process for publications, grants, patents and clinical trials.
Recent NCCN Guidelines updates—along with the supporting evidence—will be presented during the NCCN 2024 Annual Conference held in Orlando, Florida April 5-7, and simultaneously online. Visit NCCN.org/conference to learn more and register.
Unlike existing work, which relies on training data from social media examples, a new benchmark, named ToxicChat, is based on examples gathered from real-world interactions between users and an AI-powered chatbot. ToxicChat is able to weed out queries that use seemingly harmless language but are actually harmful, which would pass muster with most current models.
Fengqui "Frank" Li is a computational developer at Oak Ridge National Laboratory who uses his background as an architect to expand the landscape of design for his research into building energy modeling and beyond.
Atomic force microscopy, or AFM, is a widely used technique that can quantitatively map material surfaces in three dimensions, but its accuracy is limited by the size of the microscope’s probe. A new AI technique overcomes this limitation and allows microscopes to resolve material features smaller than the probe’s tip.
Scientists from the National University of Singapore (NUS) have pioneered a new methodology of fabricating carbon-based quantum materials at the atomic scale by integrating scanning probe microscopy techniques and deep neural networks. This breakthrough highlights the potential of implementing artificial intelligence at the sub-angstrom scale for enhanced control over atomic manufacturing, benefiting both fundamental research and future applications.
In a paper in the prestigious journal Science to appear on Feb. 29, 2024, a multi-institutional team led by scientists at Carnegie Mellon University and University of California at Berkeley found parts of the genome, both within genes and outside of them, that evolved and are associated with vocal learning across mammals. These elements have been linked to autism in humans.
A team at UC Davis has developed a machine-learning model that can better predict which patients are at greater risk to develop hepatocellular carcinoma.
UT Southwestern Medical Center researchers have developed an artificial intelligence (AI) method that writes its own algorithms and may one day operate as an "automated scientis" to extract the meaning behind complex datasets.
Digital Science has awarded two new Catalyst Grants of £25,000 each to innovative AI-based technology ideas aimed at advancing global research.
Continuous, unobtrusive sensors and related monitoring devices are installed in older drivers’ vehicles to detect changes in highly complex activities over time. A driver facing camera, forward facing camera, and telematics unit provide video in real-time to enable researchers to analyze abnormal driving such as getting lost, reaction time and braking patterns as well as travel patterns such as miles driven, miles during the night and daytime, and driving in severe weather. Detecting changes in behavior could generate early warning signs of possible changes in cognition.
A recent study introduce a novel paradigm combining ChatGPT with machine learning (ML) to significantly ease the application of ML in environmental science. This approach promises to bridge knowledge gaps and democratize the use of complex ML models for environmental sustainability.
Digital Science announces two new products – Dimensions Research GPT and Dimensions Research GPT Enterprise – bringing the unmatched, trusted research coverage of Dimensions to the ChatGPT platform.
Researchers at the Icahn School of Medicine at Mount Sinai and others have harnessed the power of machine learning to identify key predictors of mortality in dementia patients. The study, published in the February 28 online issue of Communications Medicine, addresses critical challenges in dementia care by pinpointing patients at high risk of near-term death and uncovers the factors that drive this risk. Unlike previous studies that focused on diagnosing dementia, this research delves into predicting patient prognosis, shedding light on mortality risks and contributing factors in various kinds of dementia.
Dartmouth researchers report they have developed the first smartphone application that uses artificial intelligence paired with facial-image processing software to reliably detect the onset of depression before the user even knows something is wrong.
Bridging precision engineering and precision medicine to create personalized physiology avatars. Pursuing on-demand tissue and organ engineering for human health. Revolutionizing neuroscience by using AI to engineer advanced brain interface systems. Engineering the immune system for health and wellness. Designing and engineering genomes for organism repurposing and genomic perturbations.
Two leading multiple sclerosis (MS) experts—Nancy Sicotte, MD, director of Multiple Sclerosis and Neuroimmunology at Cedars-Sinai, and Pascal Sati, PhD, director of the Neuro Imaging Program in the Department of Neurology—are attending the Americas Committee for Treatment and Research in Multiple Sclerosis Forum 2024 Feb. 29-March 2 in West Palm Beach, Florida.
The Technology Infrastructure for Data Exploration (TIDE) project at SDSU will give CSU researchers access to new high-performance data processing capabilities.
Researchers at the University of Notre Dame conducted a study using AI bots based on large language models and asked human and AI bot participants to engage in political discourse. Fifty-eight percent of the time, the participants could not identify who the AI bots were.
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