news, journals and articles from all over the world.
Building on an enduring commitment to graduate and medical education, Cedars-Sinai has established Cedars-Sinai Health Sciences University to prepare generations of academic scientists, physician leaders and allied health professionals for the future of healthcare and biomedical science.
A study led by Adam Rodman, MD, MPH, Director of AI Programs at Beth Israel Deaconess Medical Center (BIDMC), reveals that, rather than helping to reduce racial and ethnic biases, AI-driven chatbots may instead perpetuate and exacerbate disparities in medicine.
From artificial intelligence (AI) and data integration to natural language processing and statistics, the Cedars-Sinai Department of Computational Biomedicine is utilizing the latest technological advances to find solutions to some of the most complex healthcare issues.
The National Science Foundation (NSF) has awarded SMU engineering professor Digvijay Boob a five-year CAREER grant to pioneer quicker, streamlined solutions that could speed up how AI learns from data to make predictions and decisions.
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.
Two new studies by Cedars-Sinai investigators support using artificial intelligence (AI) to predict sudden cardiac arrest—a health emergency that in 90% of cases leads to death within minutes.
Researchers have developed a new deep learning model that promises to significantly improve audio quality in real-world scenarios by taking advantage of a previously underutilized tool: human perception.
While it can take years for the pharmaceutical industry to create medicines capable of treating or curing human disease, a new study suggests that using generative artificial intelligence could vastly accelerate the drug-development process.
WASHINGTON (May 4, 2023) – The Biden administration announced a slate of initiatives aimed at reducing the risks posed by rapidly advancing artificial intelligence systems. The list of measures includes $140 million for new AI research centers and a promise to…
Scientists from BFU have determined that medical professionals favor using simpler machine learning algorithms rather than deep learning AI
Artificial intelligence has made a giant leap into our rubbish bins, with smart bin sensors now providing useful information that can be fed into a neural model, helping authorities to make waste collection more efficient, sustainable, and healthier.
In this episode of Sharp Waves, the ILAE podcast, Dr. Maryam Nabavi Nouri talks with Dr. Konrad Wagstyl about the MELD Project, an open-science consortium using deep learning principles to develop automated lesion detection of clinical MRI data.
Researchers led by Jeffrey Skolnick have designed a new AI-based “decision prioritization tool” that combines data on protein pathways with common Covid-19 side effects and known patient comorbidities. The tool offers possible targeted treatment options with existing FDA-approved drugs to foster better health outcomes for individuals fighting Covid-19.
The EVONANO platform allows scientists to grow virtual tumours and use artificial intelligence to automatically optimise the design of nanoparticles to treat them.
The National Science Foundation (NSF) announced today an investment of $220 million to establish 11 artificial intelligence (AI) institutes, each receiving $20 million over five years. One of these, The Institute for Learning-enabled Optimization at Scale (TILOS), will be led by the University of California San Diego.
Researchers at the Department of Energy’s SLAC National Accelerator Laboratory have demonstrated that they can use machine learning to optimize the performance of particle accelerators by teaching the algorithms the basic physics principles behind accelerator operations – no prior data needed.
Nobody likes driving in a blizzard, including autonomous vehicles. To make self-driving cars safer on snowy roads, Michigan Tech engineers look at the problem from the car’s point of view–its sensors.
Computer scientists at the University of Illinois Chicago are developing a computational artificial intelligence system they hope will serve as a decision support tool for doctors prescribing treatment for head and neck cancer. The work is supported by a $2.8 million grant from the National Institutes of Health.
This project proposes a flexible bus dispatching system using automated modular vehicle technology, and considers multimodal interactions and congestion propagation dynamics. This study proposes a novel flexible bus dispatching system in which a fleet of fully automated modular bus units, together with conventional…
In a collaboration between Pacific Northwest National Laboratory and the University of Washington’s Urban Freight Lab, a prototype webapp has been developed that combines smart sensors and machine learning to predict parking space availability. The prototype is ready for initial testing to help commercial delivery drivers find open spaces without expending fuel and losing time and patience.
For AI to continue to transform cancer diagnoses, researchers will have to prove that the success of their machine-learning tools can be reproduced from site to site and among different patient populations. Biomedical engineering researchers at Case Western Reserve University say they doing just that. They say they have demonstrated that their novel algorithms for distinguishing between benign and malignant lung cancer nodules on CT scan images from one site can now be successfully reproduced with patients from other sites and locations.
New research that garnered a Best White Paper award at the 2021 Wharton Analytics Conference shows a way to harness artificial intelligence and machine learning tools to build a more equitable workforce.
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