Articles tagged with Artificial Intelligence
Researchers used AI to identify a compound that kills Acinetobacter baumannii, a bacterium responsible for many drug-resistant infections. The new antibiotic shows promise in combating this growing public health threat.
A nationwide VR research project is working to create diverse groups of participants to reflect real-world social dynamics. The Virtual Experience Research Accelerator (VERA) aims to provide researchers with access to large, reliable, and diverse groups for various VR research projects.
Researchers at MIT have developed a new approach to match 3D shapes by mapping volumes to volumes, resulting in more accurate animations and CAD designs. This method represents shapes as tetrahedral meshes that include the mass inside a 3D object, allowing for better modeling of fine parts and avoiding common artifacts.
A new AI framework developed by Danish researchers significantly improves the accuracy of early stroke identification by human emergency call handlers. The study found that the AI framework achieved a recall of 63.0% and precision of 24.9%, compared to 52.7% and 17.1% for human handlers, respectively.
Researchers at Rensselaer Polytechnic Institute and Albany Medical College will use AI to develop a novel mesoscopic, multimodal preclinical imaging approach to test the hypothesis that drug distribution mediates tumor resistance. The goal is to improve targeted therapy for HER2-positive breast cancer.
A new camera technology developed by Aarhus University and Newtec Engineering A/S aims to make it easier to recycle plastic materials. The technology uses hyperspectral imaging to analyze the chemical composition of plastic waste, allowing for the removal of unwanted additives that may be banned or harmful.
Researchers investigate the potential of ChatGPT in clinical radiology, highlighting its positive considerations, such as enhancing patient care and radiologist education, as well as negative concerns, like accuracy and transparency issues. The paper aims to initiate an engaging discussion on the use of AI-powered chatbots in this field.
AI enhances drug discovery by analyzing abundant data, identifying new targets and designing effective treatments, significantly reducing cycle time and costs. AI also improves clinical trial design and post-market surveillance, leading to safer and more personalized medications.
A new CAR T cell design approach using machine learning and artificial intelligence is being developed to improve cancer treatment. The project aims to create a hybrid knowledge- and data-driven approach to guide the design of immunotherapeutic cells.
Researchers used deep learning to analyze drone images of pine trees, detecting pine processionary moth larvae nests with high accuracy. The method can be applied in various settings to inform tree health managers about potential threats.
A new AI-enabled forecasting model can predict ENSO events for up to 22 months, overcoming the limitations of previous models. The Spatio-Temporal Information Extraction and Fusion (STIEF) model uses deep learning to extract space and time features and fuse them together, providing a more accurate prediction length.
A deep learning-based Transformer model, Bloomformer-1, has been developed to identify the driving factors of algal growth in the Middle Route of the South-to-North Water Diversion Project. The results reveal that total phosphorus is the most significant factor affecting algal growth, especially in the Henan section, while total nitrog...
Researchers used machine learning to identify 'synthetic extreme' DNA sequences that are active in humans but not fruit flies. These rare sequences have potential practical applications in biotechnology and biomedical research.
A study found that a conversational artificial intelligence program can generate easy-to-understand, scientifically adequate answers to common patient questions about colonoscopy. The program outperformed human ratings in terms of scientific adequacy and satisfaction with the answers.
Researchers developed AI model QUADL that can create online assessment questions indistinguishable from human-written ones. Instructors found QUADL's questions as effective as those written by humans in assessing student learning objectives. The study suggests QUADL can be a useful tool for instructors and course developers.
A new pilot study led by University of Illinois Chicago researchers found that an AI voice-based virtual coach improved depression and anxiety symptoms, as well as problem-solving skills, in patients. The Lumen app delivered a form of psychotherapy, resulting in changes in brain activity and reduced psychological distress.
Researchers propose a four-step process for guiding the use of ChatGPT in education, identifying desired outcomes, determining automation levels, ensuring ethical considerations, and evaluating effectiveness. The study finds that ChatGPT can improve teaching models, assessment systems, and student learning experiences.
Researchers found that individuals with high expectations of AI and brain-computer interfaces take greater risks despite no actual improvement. Expectations-based decision-making can lead to negative consequences, emphasizing the need for accurate evaluation and validation of new technologies.
A new machine learning-based model predicts individual cardiac surgery patient mortality risk with improved performance over current population-derived models. The model uses electronic health records to provide personalized risk assessments, offering a significant advantage over existing benchmarks.
The National Institutes of Health is enrolling 10,000 participants in a landmark precision nutrition study. The study aims to develop algorithms predicting how individuals respond to different foods and diets based on genetic, lifestyle, and environmental factors.
Large language models like ChatGPT show incredible potential in radiology, but also struggle with reliability and accuracy. The latest model GPT-4 improves advanced reasoning capabilities and performs better on higher-order thinking questions.
A diagnostic study involving 16 radiologists and 2,054 images suggests that optimized AI strategy reduces diagnostic time-based costs without sacrificing accuracy for senior radiologists. Traditional all-AI strategy may still be more beneficial for junior radiologists.
Researchers at the University of Waterloo have developed a robot system that uses artificial intelligence to track objects, including medication and glasses. This technology has shown high accuracy in locating everyday items, potentially improving the quality of life for individuals with dementia.
A new artificial intelligence algorithm, SSAFS, uses handcrafted image features for accurate plant disease detection and severity estimation. The algorithm outperforms existing state-of-the-art algorithms in identifying valuable disease-related features.
Researchers developed an AI-powered tool to identify and measure coral reef halos globally, providing a new method for monitoring ecosystem health. The tool enables efficient tracking of reef ecosystems' function at large scales, improving understanding and management of coral reefs.
An AI developed at TU Wien has shown to suggest appropriate treatment steps in cases of blood poisoning, outperforming human decisions. The AI can examine time-varying patient conditions and calculate treatment strategies, increasing cure rates by up to 3%. However, legal aspects and liability need discussion.
Researchers found that highly work-relevant personal information from an AI agent elicits greater empathy in users, while less or no disclosure leads to suppressed empathy. This study suggests self-disclosure could be used to improve people's acceptance of AI technologies.
Researchers found that machine-learning models trained with descriptive data label rule violations more harshly than humans, leading to potential serious implications in the real world. This study highlights the need for careful consideration of data labeling and training methods to ensure fairness and accuracy in AI decision-making.
The article highlights the risks of AI misuse, including its impact on democracy, job loss, and social division. Experts warn that self-improving AGI poses an existential threat to humanity, and call for regulation to minimize harm and maximize benefits.
Researchers used AI to study how the hippocampus produces varied replay types efficiently and their purpose. The model showed that sequences are prioritized stochastically according to familiarity and reward positions.
Researchers used large language models to generate open-ended answers, which were often more convincing than real responses. This approach may help gather data quickly and at low cost but also raises concerns about the authenticity of online user data.
A team of researchers developed an unsupervised entity alignment framework to improve knowledge graph search, avoiding human labor. The framework outperformed most competitors on precision and recall, scoring higher overall across multiple datasets.
A team of researchers has developed an AI tool called CRANK-MS that uses neural networks to analyze biomarkers in patients' bodily fluids and predict Parkinson's disease onset with an accuracy of up to 96%. The tool may help identify early warning signs for the disease, which can be challenging to diagnose.
Researchers used AI tools to simplify a test for hepatitis C and SARS-CoV-2, achieving 97% accuracy for SARS-CoV-2 and 95% accuracy for the most prevalent version of hepatitis C. The team plans to refine the test, improve its ability to distinguish between strains, and potentially develop at-home tests in the future.
Researchers developed EmbryoNet, an automated image analysis software that uses AI to detect and classify developmental defects in fish embryos. The software outperforms human experts in terms of speed and accuracy, making it a valuable tool for investigating the mechanisms of drug action and studying embryonic development.
Researchers from Integrated Biosciences developed an AI platform to discover novel senolytic compounds, a class of molecules targeting age-related processes. The platform identified three highly selective and potent compounds with favorable medicinal chemistry properties.
Researchers developed an online adaptive model for streaming anomaly detection that incorporates human-machine cooperation. The ISPForest method adjusts parameters and structures in real-time based on human feedback, leading to improved accuracy and reduced labor costs.
Researchers have developed a system that uses generative diffusion to create new proteins, advancing the field of generative biology. The system, called ProteinSGM, learns from image representations to generate fully new proteins, which are biophysically real and functional.
A diagnostic study using 4,095 retinal fundus images found that biomarker-based AI algorithms can be susceptible to racial bias, even when trained on raw images. This issue highlights the need for careful evaluation of AI performance in diverse populations.
The National Science Foundation has awarded Columbia University a $20 million grant to establish the AI Institute for Artificial and Natural Intelligence (ARNI), an interdisciplinary center focused on connecting AI systems to brain research. ARNI will bring together top researchers from across the U.S. to advance neuroscience, cognitiv...
The UMD-led TRAILS institute will develop AI technologies that promote trust and mitigate risks through broader participation, new technology development, and informed governance. The institute aims to create AI systems that align with values and interests of diverse groups, leading to increased transparency, reliability, and accountab...
Researchers have developed a new method called EvoAug that uses artificial DNA sequences inspired by evolution to train deep neural networks for genome analysis. This approach enables the model to recognize regulatory motifs more accurately, leading to better performance and potential breakthroughs in understanding human health.
Researchers identified three novel dual-purpose therapeutic targets using PandaOmics, which could treat both aging and glioblastoma multiforme. The target hypotheses include cyclic nucleotide gated channel subunit alpha 3 (CNGA3), glutamate dehydrogenase 1 (GLUD1) and sirtuin 1 (SIRT1).
A study published in Radiology found that AI-based decision support systems can impair radiologist accuracy on mammograms, particularly for less experienced radiologists. Even highly experienced radiologists were adversely impacted by the system's judgments, highlighting the need for safeguards to mitigate automation bias.
Researchers from Baidu Research have developed an AI algorithm, LinearDesign, that boosts COVID-19 mRNA vaccine antibody response by 128-fold. The algorithm takes a mere 11 minutes to generate the most stable mRNA sequence encoding Spike protein.
Researchers found that using positive trigger words can retrain large language models and result in less biased responses. The team analyzed GPT-2's responses to user prompts about different countries worldwide and found a significant impact on the types of adjectives used to describe citizens.
Three high school students co-authored a paper using AI engine PandaOmics to discover new therapeutic targets for glioblastoma multiforme, a common and aggressive malignant brain tumor. The study identified three genes strongly correlated with both aging and glioblastoma as potential therapeutic targets.
A study published in Radiology found that AI can analyze breast mass images from low-cost portable ultrasound machines and accurately identify cancer. The technology showed great promise for improving breast health care in low-resource settings, reducing delays in diagnosis and potentially saving lives.
Researchers at the University of Texas at Austin developed a semantic decoder that translates brain activity into text, allowing individuals with speech disabilities to communicate. The system has been trained on extensive hours of podcasts and can decode continuous language, capturing the gist of what is being said or thought.
The Connected Minds program aims to balance emerging tech's benefits and risks for humanity, with a focus on promoting equity and inclusivity. York University has received $82.8 million in federal funding through the Canada First Research Excellence Fund.
A new feature selection method for text categorization, Max-difference Maximization Criterion (MDMC), is proposed to select discriminant terms. MDMC combines a weight based on class information occupacity with ACC2 to estimate term importance, avoiding overestimation of sparse terms.
Researchers at Sainsbury Wellcome Centre found that instinctual exploratory runs enable mice to learn a map of the world efficiently. The study demonstrates how biological brains can learn faster and more efficiently than AI agents by focusing on salient objects.
A new MIT deep-learning system can analyze the internal structure and properties of materials based solely on their surface conditions. The technique uses vast amounts of simulated data to generate reliable predictions, offering a promising solution for engineers seeking non-invasive insights into material properties.
A study comparing physician and AI chatbot responses found chatbots excelled in both quality and empathy, while physicians were preferred for their nuanced understanding. The findings suggest that integrating chatbots into clinical settings could improve response quality and reduce clinician burnout.
A recent study assessed an AI chatbot's ability to answer ophthalmic questions, finding it answered approximately half correctly. The results suggest that while AI has medical advances, the current chatbot is not yet ready for substantial assistance in board certification preparation.
A team of IUPUI researchers has developed an AI-powered approach to classify insect species, tackling the challenge of discovering new species. The method uses deep hierarchical Bayesian learning to distinguish between known and unknown species, providing insight into their taxonomy and ecosystem impacts.
A deep learning AI model has been developed to screen for retinopathy of prematurity (ROP) in infants at risk of blindness. The tool was trained on images of newborns and found to be as effective as senior paediatric ophthalmologists in discriminating normal retinal images from those with ROP that could lead to blindness.
A recent study published in Sleep Medicine warns that AI-collected data may lead to misdiagnosis of childhood sleep disorders. The lack of pediatric data used to train AI models results in biased classification, causing errors in sleep stage identification and potentially severe consequences for young patients.
A study by Pohang University of Science & Technology (POSTECH) analyzed water pollution complaints in Alabama and found a significant decrease during the COVID-19 pandemic. The research team used AI methodologies to examine sentiment changes and correlations with climatic extremes.
Researchers at Argonne National Laboratory have developed a self-driving laboratory called Polybot, which automates electronic polymer research and frees scientists' time to work on tasks only humans can accomplish. The tool combines AI and robotics to streamline experimental processes and accelerate discovery.