Articles tagged with Artificial Intelligence
Researchers developed MOVA to evaluate the pathogenicity of missense variants based on their structural position. The new method showed superior performance in predicting pathogenicity, especially for hotspot mutations in ALS-causing genes like TARDBP and FUS.
The Mori3 robot is a polygon shape-shifting robot designed for space travel, capable of morphing into any 3D object and adapting to various environments. Its versatility makes it an ideal candidate for communication and external repairs in spacecraft.
A new study published in JMIR Cardio reveals the potential of AI-QCA in analyzing coronary angiography, providing accurate measurements of coronary stenotic lesions similar to IVUS. This technology has the potential to improve patient outcomes and support clinical decision-making.
Research by the American Psychological Association found that employees who frequently interact with AI systems are more likely to experience loneliness, insomnia, and increased after-work alcohol consumption. However, these effects may be mitigated by equipping AI systems with social features or limiting their use.
Researchers developed Eye2Gene, an AI system that accurately identifies genetic causes of inherited retinal diseases from retinal scans. The system achieved higher accuracy than human experts in identifying causative genes, with a ranking score above 70% in over 70% of cases.
Researchers have developed a new AI model that can quickly screen large libraries of potential drug compounds against target proteins. The ConPLex model uses language analysis to match potential drugs with proteins without needing to calculate molecular structures, enabling fast screening of over 100 million compounds per day.
MethaneMapper is an artificial intelligence-powered hyperspectral imaging tool that can detect real-time methane emissions and trace them to their sources. With a performance accuracy of 91%, it has the potential to revolutionize the way we monitor oil and gas operations and curb climate change.
Researchers developed an AI model to estimate best-corrected visual acuity from fundus photographs in patients with diabetic macular edema, achieving accuracy within one to two lines on a standard chart. The study's findings support the potential of AI in clinical settings, with opportunities for further improvement.
ChatGPT achieved high scores in clinical reasoning and demonstrated rapid response times to evolving questions. However, its lack of local ethnic knowledge and limited medical references hindered its performance in highly contextual situations.
Researchers found that manual labor's perceived meaning can predict the adoption of autonomous products. Consumers tend to reject these products due to a loss of meaning in life. Companies can highlight alternative sources of meaning to reduce negative effects on adoption.
ChatGPT consistently provided accurate, evidence-based answers in public health questions, beating rival AI assistants. Meanwhile, Amazon Alexa and Google Assistant struggled to recognize or refer users for help.
Researchers at NYU Grossman School of Medicine have developed an AI tool called NYUTron that can accurately estimate patients' risk of death, length of hospital stay, and other factors important to care. The tool achieved impressive results in predicting readmission rates, improving upon standard methods by up to 7%.
Researchers at TU Delft and EPFL collaborated with ChatGPT to design a tomato-harvesting robot, showcasing the potential of AI in robotics. The study highlights the need for verification and validation of LLM output to prevent misinformation and bias.
A study by the University of California - San Diego found that ChatGPT provided evidence-based responses to 91% of public health questions, but only referred users to resources in 22% of cases. The researchers emphasize the importance of promoting human-powered critical resources like life-saving hotlines.
Researchers at KAUST developed smart digital image sensors that can recognize images with high accuracy, using a charge-trapping 'in-memory' sensor sensitive to visible light. The devices have an extremely long-lived retention time of up to 10 years and can perform optical sensing, storage, and computation.
A University of Minnesota team developed a new superconducting diode that is more energy efficient and versatile than past models. The device can process multiple electrical signals at once and has gates to control the flow of energy, which could enable faster quantum computers for industry use and enhance AI performance.
Researchers developed a new AI framework that significantly improves the ability to analyze team communication, enabling adaptive training technologies to facilitate effective team collaboration. The framework performed substantially better than previous AI technologies in classifying dialogue and following information flow.
A recent study published in Radiology found that AI algorithms performed better than the standard Breast Cancer Surveillance Consortium (BCSC) risk model for predicting five-year breast cancer risk. The AI models extracted hundreds of additional mammographic features, leading to improved predictive performance.
Researchers develop a novel voice spoofing detection method combining physiological and physical features, achieving improved performance over existing methods. The proposed method outperforms single systems in terms of EER and t-DCF scores.
Scientists using popular computational tools to interpret AI predictions are picking up too much 'noise' when analyzing DNA. Researchers have found a way to fix this by applying a new line of code, leading to more reliable explanations and potentially unlocking the next breakthrough in health and medicine.
A team of University of Colorado School of Medicine researchers explores the impact of chatbot avatars on patient experience, trust, and compassion in healthcare. They found that chatbots may reinforce social stereotypes and biases, but also have potential to increase trust among underserved patient groups if designed with respect for ...
Researchers developed an AI-powered mass spectrometric technique to determine a polymer's monomeric sequence. This breakthrough may lead to improved materials performance and tackle plastic recycling issues.
Researchers updated their protein localization prediction model, MULocDeep, to provide more targeted predictions for biological discoveries. The tool helps researchers design more effective experiments and advance scientific discoveries related to drug development and treating diseases like epilepsy.
A new Danish research project, CP-SENS, aims to develop a digital twin platform for the mechanical and construction industries. The project will provide companies with access to intelligent IT systems tailored to their needs, enabling them to adopt digital twins without significant financial investment.
Researchers at UCLA Health highlight the impact of test results on lung cancer screening adherence and the potential unnecessary use of axillary ultrasound in breast cancer diagnostics. Meanwhile, a new AI system helps check endotracheal tube placement with high accuracy and user satisfaction.
FES2023 brings together leading researchers and industry experts to share groundbreaking research on AI-based energy solutions, smart grids, and renewable energy systems. The conference covers key themes including planning and operation of energy systems, electricity markets, and demand response.
Researchers at North Carolina State University have developed a new methodology called Patch-to-Cluster attention (PaCa) that addresses the challenges of vision transformers. PaCa improves ViT's ability to identify, classify, and segment objects in images while reducing computational demands and enhancing model interpretability.
Researchers develop AI-based method to quantify cracking patterns in reinforced concrete structures, enabling more accurate and efficient assessments of structural damage. The approach uses graph theory and machine learning algorithms to create a unique 'fingerprint' for each set of cracks, allowing for quick and consistent evaluations.
A WVU researcher is developing a data-driven approach to enhance roadway maintenance operations, incorporating AI analysis of real-time data from sensors on consumer cars and roadside observation stations. The goal is to minimize time and resources spent on less affected roadways, reducing winter maintenance costs and time.
Researchers developed an AI system, Geneformer, to predict how disruptions in human gene connections cause disease. The model, trained on data from thousands of genes, can identify potential drug targets for diseases like heart disease and cancer.
Researchers at Ohio State University developed an AI framework called G2 Retro to automatically generate chemical reactions for molecules. The framework was shown to cover a vast range of possible reactions and accurately predict the best synthesis routes, offering more efficient drug design options.
A new study published in The Neuroradiology Journal introduces an artificial intelligence computer program that can accurately identify changes in brain structure resulting from repeated head injury. This AI tool uses machine learning to process magnetic resonance imaging (MRI) scans and distinguish between the brains of male athletes ...
A deep learning-based framework called EMGSense enables accurate wearable EMG device usage through AI self-training techniques. It achieves average accuracy of 91.9% in gesture recognition and 81.2% in activity recognition, outperforming state-of-the-art approaches.
Researchers at the Beckman Institute for Advanced Science and Technology have developed a new framework for super-resolution ultrasound using deep learning, reducing processing speeds from minutes to seconds. The new technology enables real-time blood flow visualization, overcoming challenges faced by conventional methods.
A deep learning system accurately classifies autism spectrum disorder and predicts symptom severity based on behavioral responses to social cues. The method offers a digital measurement of joint attention, but further validation is needed.
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.