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UrduSmith is a nationally recognized academic radiologist with expertise in body and oncologic imaging, clinical trials and imaging research and the application of artificial intelligence (AI) in imaging and medicine.
Tag: Machine Learning
How Scientists Are Accelerating Chemistry Discoveries With Automation
Researchers have developed an automated workflow that could accelerate the discovery of new pharmaceutical drugs and other useful products. The new approach could enable real-time reaction analysis and identify new chemical-reaction products much faster than current laboratory methods.
Moffitt Cancer Center to Revolutionize Cancer Care Delivery Using AI and Machine Learning with NVIDIA, Oracle and Deloitte
Moffitt Cancer Center announced today a collaboration with NVIDIA, Oracle and Deloitte* on an initiative aimed at revolutionizing cancer care delivery through advanced artificial intelligence and machine learning technologies.
Q&A: How to train AI when you don’t have enough data
As researchers explore potential applications for AI, they have found scenarios where AI could be really useful but there’s not enough data to accurately train the algorithms. Jenq-Neng Hwang, University of Washington professor of electrical and computer and engineering, specializes in these issues.
The Time Is Now for Artificial Intelligence, Machine Learning
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.
Domain knowledge drives data-driven artificial intelligence in well logging
In well logging interpretation, researchers incorporate logging response functions that incorporate domain knowledge into the loss function of data-driven machine learning models, which are used to constrain model outputs.
Study Estimates Nearly 70 Percent of Children Under Six in Chicago May Be Exposed to Lead-Contaminated Tap Water
A new analysis led by researchers at the Johns Hopkins Bloomberg School of Public Health estimates that 68 percent of Chicago children under age six live in households with tap water containing detectable levels of lead.
Revolutionizing Carbon Neutrality: Machine Learning Paves the Way for Advanced CO2 Reduction Catalysts
A perspective highlights the transformative impact of machine learning (ML) on enhancing carbon dioxide reduction reactions (CO2RR), steering us closer to carbon neutrality.
SMU Chemist and Colleagues Develop Machine Learning Model for Atomic-level Interactions
Machine learning interatomic potentials (MLIP)s have become an efficient and less expensive alternative to traditional quantum chemical simulations.
Machine learning algorithm identifies individuals who experience the largest reduction in depression risk from Medicaid coverage
Previous research has demonstrated that Medicaid coverage reduces the risk for developing depression among recipients, but the question is who benefits most from coverage. Using a tool called machine learning causal forest to analyze data from the Oregon Health Insurance…
The role of machine learning and computer vision in Imageomics
A new field promises to usher in a new era of using machine learning and computer vision to tackle small and large-scale questions about the biology of organisms around the globe.
UTSW team’s new AI method may lead to ‘automated scientists’
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.
JMIR Neurotechnology Invites Submissions on Brain-Computer Interfaces (BCIs)
JMIR Publications is pleased to announce a new theme issue in JMIR Neurotechnology exploring brain-computer interfaces (BCIs) that represent the transformative convergence of neuroscience, engineering, and technology.
Widely used machine learning models reproduce dataset bias in Rice study
High-income communities overrepresented in relevant datasets for immunotherapy research.
Imageomics poised to enable new understanding of life
Imageomics, a new field of science, has made stunning progress in the past year and is on the verge of major discoveries about life on Earth, according to one of the founders of the discipline.
Tanya Berger-Wolf, faculty director of the Translational Data Analytics Institute at The Ohio State University, outlined the state of imageomics in a presentation at the annual meeting of the American Association for the Advancement of Science.
Shuffling the deck for privacy
By integrating an ensemble of privacy-preserving algorithms, a KAUST research team has developed a machine-learning approach that addresses a significant challenge in medical research: How to use the power of artificial intelligence (AI) to accelerate discovery from genomic data while protecting the privacy of individuals
Q&A: What is the best route to fairer AI systems?
Mike Teodorescu, a University of Washington assistant professor in the Information School, proposes that private enterprise standards for fairer machine learning systems would inform governmental regulation.
Researchers Characterize the Immune Landscape in Cancer
Researchers from the Icahn School of Medicine at Mount Sinai, in collaboration with the Clinical Proteomic Tumor Analysis Consortium of the National Institutes of Health, The University of Texas MD Anderson Cancer Center, Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine, and others, have unveiled a detailed understanding of immune responses in cancer, marking a significant development in the field. The findings were published in the February 14 online issue of Cell. Utilizing data from more than 1,000 tumors across 10 different cancers, the study is the first to integrate DNA, RNA, and proteomics (the study of proteins), revealing the complex interplay of immune cells in tumors. The data came from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), a program under the National Cancer Institute.
Argonne scientists use AI to identify new materials for carbon capture
Researchers at the U.S. Department of Energy’s Argonne National Laboratory have used new generative AI techniques to propose new metal-organic framework materials that could offer enhanced abilities to capture carbon
Argonne training program alumni find success in extreme-scale computing
Past attendees of the annual Argonne Training Program on Extreme-Scale Computing are thriving in careers across the field of high performance computing.
Mount Sinai Researchers Awarded $4.1 Million NIH Grant to Advance Understanding of Sleep Apnea Using Artificial Intelligence
Machine-learning method aims to predict consequences of serious sleep disorder impacting millions in the U.S.
How the Quantum World Can Help Scientists Engineer Biology
By studying how CRISPR-Cas works, scientists can predict and design where these tools modify DNA.
Q&A: Helping robots identify objects in cluttered spaces
Robots in warehouses and even around our houses struggle to identify and pick up objects if they are too close together, or if a space is cluttered.
AI can use human perception to help tune out noisy audio
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.
Cedars-Sinai High-Risk Pregnancy Experts Share Latest Research at Annual Scientific Meeting
High-risk pregnancy specialists from Cedars-Sinai will share their research findings at the Society for Maternal-Fetal Medicine 2024 Pregnancy Meeting, Feb.10-14, in National Harbor, Maryland.
Artificially intelligent software provides a detailed look at jets of plasma used to treat cancer
Artificially intelligent software has been developed to enhance medical treatments that use jets of electrified gas known as plasma. Developed by researchers at Princeton Plasma Physics Laboratory and the George Washington University, the computer code predicts the chemicals emitted by cold atmospheric plasma devices, which can be used to treat cancer and sterilize surfaces.
Machine learning to battle COVID-19 bacterial co-infection
University of Queensland researchers have used machine learning to help predict the risk of secondary bacterial infections in hospitalised COVID-19 patients.
Promising heart drugs ID’d by cutting-edge combo of machine learning, human learning
University of Virginia scientists have developed a new approach to machine learning – a form of artificial intelligence – to identify drugs that help minimize harmful scarring after a heart attack or other injuries.
New AI Technique Significantly Boosts Medicare Fraud Detection
In Medicare insurance fraud detection, handling imbalanced big data and high dimensionality remains a significant challenge. Systematically testing two imbalanced big Medicare datasets, researchers demonstrate that intelligent data reduction techniques improve the classification of high imbalanced big Medicare data.
Machine sentience and you: what happens when machine learning goes too far
There’s always some truth in fiction, and now is about the time to get a step ahead of sci-fi dystopias and determine what the risk in machine sentience can be for humans.
American nuclear power plants are among the most secure in the world — what if they could be less expensive, too?
Argonne collaborates with Purdue University on new research aimed at lowering the cost of developing small nuclear reactors.
A cutting-edge approach to tackling pollution in Houston and beyond
University of Houston researchers use machine learning and SHAP analysis to pinpoint air pollution sources
Argonne researchers to present cutting-edge work at SC23 conference
Argonne scientists recognized for use of exascale computing tools to achieve high-fidelity simulations of advanced nuclear reactor systems and high-resolution simulations that reduce uncertainty in climate model predictions.
Advances in machine learning for nuclear power operations spell a brighter future for carbon-free energy
Researchers at Argonne are harnessing the power of machine learning to enhance the safety and efficiency of next-generation nuclear reactors. Using a specialized model, researchers may be able to detect anomalies in reactor operations even when they are masked by other noises, ensuring a safer energy future.
Learning to forget – a weapon in the arsenal against harmful AI
In a world increasingly aware of the environmental challenges posed by microplastics, a pioneering study conducted by Ruxandra Malina Petrescu-Mag from Babes-Bolyai University, and published in PeerJ Life & Environment, sheds new light on the impact of media narratives on public perception and awareness of microplastic risks.
Scientists reveal structures of neurotransmitter transporter
Scientists at St. Jude Children’s Research Hospital determined structures of a transporter protein involved in the movement of neurochemicals such as serotonin and dopamine, unearthing multiple mechanisms that can guide drug development.
UAlbany Expert Available to Discuss President Biden’s Executive Order on AI
ALBANY, N.Y. (Nov. 1, 2023) — On Monday, President Biden issued a new executive order on “Safe, Secure, and Trustworthy Artificial Intelligence,” aimed at ensuring the United States leads the way in leveraging the promise of the technology, while also…
The AI Revolution: Surgeons Share Insights on Integrating AI into Surgical Care
A panel of leading surgeons convened recently to discuss the transformative role of artificial intelligence (AI) in modern surgical practices. The surgeons, all pioneers in adopting AI into their work and studying potential applications, illustrated how this technology is revolutionizing patient care before, during, and after surgery.
Inspection method increases confidence in laser powder bed fusion 3D printing
Researchers at the Department of Energy’s Oak Ridge National Laboratory have improved flaw detection to increase confidence in metal parts that are 3D-printed using laser powder bed fusion.
A new era for accurate, rapid COVID-19 testing
Research from Osaka University demonstrates a nanopore-based technique that can detect different variants of SARS-CoV-2, the virus that causes COVID-19. The method was very effective in detecting the Omicron variant of the virus in the saliva of people with COVID-19.
Artificial intelligence may help predict infection risks after implant-based breast reconstruction
Artificial intelligence (AI) techniques may provide a more accurate approach to predicting the risk of periprosthetic infection after implant-based breast reconstruction, reports a study in the November issue of Plastic and Reconstructive Surgery®, the official medical journal of the American Society of Plastic Surgeons (ASPS).
A Cancer Survival Calculator Is Being Developed Using Artificial Intelligence
Researchers have developed an artificial intelligence (AI)–based tool for estimating a newly diagnosed cancer patient’s chances for surviving long term, according to a study presented at the American College of Surgeons (ACS) Clinical Congress 2023.
A revolution in the making
Argonne National Laboratory is shaping Industry 4.0 with groundbreaking research into advanced ways of making things more effective, efficient and economical, using the most cutting-edge materials and processes, with the lowest possible environmental impact.
AI Discussion at International Medical Conference Presented by Sbarro Health Research Organization
Top Italian Scientists join the discussion of AI and Machine Learning presented by the Sbarro Health Research Organization (SHRO) in collaboration with the National Italian American Foundation (NIA) this weekend at the annual NIAF convention and gala.
What Is the Impact of Predictive AI in the Health Care Setting?
Models built on machine learning in health care can be victims of their own success, according to researchers at the Icahn School of Medicine and the University of Michigan.
Researchers create a neural network for genomics—one that explains how it achieves accurate predictions
A team of New York University computer scientists has created a neural network that can explain how it reaches its predictions. The work reveals what accounts for the functionality of neural networks—the engines that drive artificial intelligence and machine learning—thereby illuminating a process that has largely been concealed from users.
UAlbany Chemist Receives $1 Million in Federal Support to Commercialize Forensic Investigation Tool
The funding will be used to advance a novel technology, which combines Raman spectroscopy and machine learning to identify body fluid traces at crime scenes.
Using artificial intelligence, Argonne scientists develop self-driving microscopy technique
Argonne researchers have tapped into the power of AI to create a new form of autonomous microscopy.
Department of Energy Announces $16 Million for Research on the DIII-D National User Facility and Small-scale Experiments
Today, the U.S. Department of Energy (DOE) announced $16 million in funding for nine projects that are focused on advancing innovative fusion technology and collaborative research on small-scale experiments and on the DIII-D National Fusion Facility, an Office of Science scientific user facility. The projects will be executed under 16 awards at 13 institutions across the nation.
Predicting condensate formation by cancer-associated fusion oncoproteins
St. Jude researchers shed light on a key player in cancer development by exploring the ability of fusion oncoproteins to form condensates in cells.