Articles tagged with Artificial Intelligence
Medical imaging foundation models revolutionize cancer precision medicine by integrating multimodal data, AI algorithms, and large language models. They enhance diagnostic accuracy and clinical interpretability, facilitating personalized therapy and intelligent clinical decision support.
Researchers have summarized recent breakthroughs in theranostic nanomaterials, engineered nanoparticles that can both diagnose and treat TBI. These materials can deliver drugs precisely where damage occurs while monitoring biological changes inside the brain.
JMIR Publications has integrated Signals Manuscript Checks into its publication process, utilizing AI-powered tools to streamline research integrity evaluations. This partnership aims to increase efficiency and quality, ensuring high standards of scientific integrity in published research.
A recent FAU Engineering study leverages quantum computing to enhance the accuracy of chronic kidney disease (CKD) diagnosis. The research team developed and compared two automated systems: Classical Support Vector Machine (CSVM) and Quantum Support Vector Machine (QSVM). CSVM achieved remarkable 98.75% accuracy, while QSVM reached 87....
A recent study finds that artificial intelligence has a negligible effect on global greenhouse gas emissions. The researchers' analysis of US energy consumption and AI use across industries revealed that the energy usage from AI in the US equals the energy consumption for all of Iceland, but not noticeable on a global scale.
Researchers from the University of Liège examined the capabilities of Midjourney and DALL·E to produce images from simple sentences, revealing both aesthetic feats and beginner's mistakes. The study highlights the limitations of translation between verbal and visual language.
Large language models systematically rate speakers of German dialects less favorably than those using Standard German, associating dialects with negative traits. The bias grows when dialects are explicitly mentioned, and larger models display even stronger biases.
Researchers develop a new AI approach to simulate the Earth system, enabling better estimates of local impacts on ecosystems and societies. The WOW project aims to couple AI models across scales of space and time, revealing hidden connections in the climate system.
The ESMO Congress on Artificial Intelligence and Digital Oncology features a diverse range of presentations on multimodal AI for clinical decision support, AI-based biomarkers for precision oncology, and digital health in practice. The congress also explores ethics, regulation, and patient perspectives.
A new report from Northwestern University offers six practical strategies to improve the doctor-patient bedside encounter in an era dominated by AI. By employing these strategies, clinicians can strengthen patient-physician relationships, combat inequities, and reduce burnout, ultimately leading to better patient outcomes.
The University of Osaka and NEC have demonstrated a wide-area distributed campus AI processing platform, enabling on-demand use of GPUs from compute servers via an optical network. This platform leverages NEC's ExpEther technology to connect and disconnect devices across campus, improving efficiency and optimizing GPU utilization.
Researchers developed CellWhisperer, an AI method and software tool that links gene expression with descriptive text across millions of biological samples. It provides a virtual AI-based colleague to support biologists in their research, making biomedical data exploration easier and more exciting.
A new study finds that generative AI can help define viable objectives for organizational and policy decision-making, but the overall quality of those objectives falls short without human intervention. The research suggests that AI tools are valuable brainstorming partners, but sound decision analysis still requires a 'human in the loop'.
Omni-modal language models integrate perception, understanding, and reasoning within a unified framework, bringing AI closer to human cognition. Lightweight adaptation strategies improve deployment efficiency in real-time medical and industrial scenarios.
A new study by Penn State researchers uses an AI model to analyze over 400 small-group conversations and finds that discussions improve critical thinking in 4th-5th graders. The approach, based on Quality Talk, enables the analysis of large amounts of qualitative data quickly and efficiently.
The Hong Kong Polytechnic University (PolyU) has achieved a breakthrough in perovskite/silicon tandem solar cells, focusing on improving efficiency, stability and scalability. The team aims to raise the energy conversion efficiency from 34% to 40%, while promoting industry-academia-research collaboration.
Researchers studied how large language models (LLMs) operate and found they use built-in patterns for tracking word positions to form internal beliefs and make social inferences. By understanding these patterns, it may be possible to make LLMs more efficient by activating only a subset of parameters.
A five-minute training can significantly improve people's ability to identify fake faces created by artificial intelligence, according to new research. The study found that super-recognisers correctly identified fake faces 64% of the time, while typical participants scored 51% accuracy.
Researchers developed an AI model that outperforms radiologists in detecting hidden objects on chest scans, particularly for cases of foreign body aspiration. The model achieves high precision and recall rates, helping doctors diagnose complex conditions more reliably.
The study found that AI's rapid growth would annually put 24-44 million metric tons of CO2 into the atmosphere, draining 731-1125 million cubic meters of water. A roadmap suggests using smart siting, faster grid decarbonization and operational efficiency to cut impacts by 73% (carbon dioxide) and 86% (water).
A PhD student at Lehigh University is working with Siemens to develop real-time monitoring and control tools for hyperscale data centers. The goal is to create a localized power network that can operate independently of the main grid, reducing power demands from artificial intelligence and increasing energy efficiency.
Researchers found that large language models exhibit systematic biases when evaluating texts, but only when the source or author is revealed. The studies showed a high level of agreement among models when no information was provided, but decreased agreement and even bias emerged when fictional sources were used.
The study reveals how chemical sensors discriminate among various odorant molecules using explainable AI, enabling the selection of receptor materials for high-performance sensors. This breakthrough advances understanding of human olfaction and paves the way for practical application of artificial olfaction technology.
The inaugural Margot and Tom Pritzker Prize for AI in Science Research Excellence recognizes outstanding contributions to the intersection of artificial intelligence and scientific research. Winners Kyle Cranmer and Deborah Marks were honored for their pioneering work in simulation-based inference and language modeling methods.
The $12.25 million NSF grant will fund the development of an advanced system prototype and tools for automation, remote data control, and scientific workflow management. The project aims to support the establishment of data centers in the region and provide access to these resources to researchers nationwide.
Researchers from Chung-Ang University have developed a novel AI-based approach for producing high-fidelity and defect-aware ultrasonic images, outperforming traditional techniques. This technology has the potential to revolutionize non-destructive testing in industries such as semiconductors, energy, and automotive.
The joint research hub aims to bridge academic research and scalable infrastructure for open AI systems, with a focus on decentralised computing architectures and secure AI marketplaces. Early proof-of-concepts are expected within two years, targeting pilot applications in finance, healthcare, and smart infrastructure.
Recent research in pathological foundation models has demonstrated robust representation learning capabilities and generalization performance. These models enable multi-task transfer with minimal data, significantly enhancing clinical utility and generalizability.
The American College of Cardiology and OpenEvidence are collaborating to integrate AI technology into clinical workflows, enhancing patient care and outcomes. The partnership aims to translate scientific discovery into clinical implementation, providing clinicians with timely and accurate medical evidence.
KVzip reduces chatbot response time and memory cost while maintaining accuracy, achieving 3–4× memory reduction and approximately 2× faster response times. The technology also demonstrates scalability to extremely long contexts and has been integrated into NVIDIA's open-source library.
The Biohub initiative combines world-class compute capacity, frontier AI research and engineering, and state-of-the-art experimental and imaging technologies to accelerate science. The combined team will develop datasets, laboratory technologies, and modeling innovations needed to build next-generation AI systems for biological discovery.
A new study reveals AI can generate highly realistic images of real people that are hard to tell apart from authentic photos. Despite familiarity with the subject's appearance, participants struggled to distinguish between genuine and fake images.
A new AI model uses machine learning to predict drug toxicity in humans by identifying biological differences between cells, mice, and humans. The model improved predictive power over existing state-of-the-art models and demonstrated practicality in predicting market withdrawal due to toxicity.
The ITU delegation will work with governments and private-sector leaders to leverage digital transformation as a catalyst for climate solutions. The Green Digital Action initiative aims to address the sector's environmental impact through practical tools and tangible results.
A new study published in the Chinese Neurosurgical Journal explores an AI tool that identifies medulloblastoma subgroups based on magnetic resonance imaging scans. The model achieved impressive accuracy in predicting molecular subtypes and genetic risk factors, with 91% accuracy for TP53 mutations and 87% accuracy for chromosome 11 loss.
The Mount Sinai Health System will implement Microsoft Dragon Copilot, an AI-powered voice assistant designed to streamline clinical documentation and automate administrative tasks. The rollout aims to reduce burnout, improve patient experiences, and enhance clinical efficiency across the system.
Researchers developed a protein language model that can generate functional antibodies against specific viral antigens. This approach enables rapid generation of antibodies against emerging health threats without requiring blood samples or antigen proteins.
Researchers created an AI algorithm that can identify obstructive sleep apnea (OSA) using ECG test results, making it faster and cheaper to spot the condition. The innovation reveals a higher visibility of OSA on ECG in females compared to males, suggesting they may suffer more cardiovascular damage.
The FAU College of Engineering and Computer Science has established the 'Ubicquia Innovation Center for Intelligent Infrastructure' to develop transformative technologies. The center will empower students and faculty to create AI-First solutions for a smarter, more connected world.
A Michigan State University-led study examines how well AI personas can detect human deception and compares their performance to humans. The results show that AI is more lie-biased and less accurate than humans, highlighting the need for improvement before generative AI can be used for deception detection.
Researchers found that non-expert users' intuitive prompts triggered biased responses as effectively as expert 'jailbreak' techniques. The study used a competition to identify biases in generative AI models and suggests strategies for mitigating issues.
A team of researchers proposes a theoretical framework to induce non-reciprocal interactions in solid-state systems using light. By applying dissipation-engineering scheme, they predict a non-equilibrium phase transition called non-reciprocal phase transition, leading to a spontaneous and continuous rotation of magnetization.
Researchers discover that super-recognisers don't just process more information, but instead focus on the most informative parts of a face to aid in recognition. This skill cannot be learned and is an automatic, dynamic way of picking up unique facial features.
A study found that undergraduate computer science students initially increase their trust in generative AI programming tools, but long-term trust remains unclear. To address this, researchers recommend that educators provide tasks-based training and ensure students comprehend and modify code without relying on AI assistants.
A UT Dallas team developed an electrochemical biosensor that accurately identifies eight volatile organic compounds linked to thoracic cancers. AI analysis enhances the accuracy of breath samples, showing promise for early lung cancer detection and improved patient outcomes.
A new empirical study by computer scientists reveals that software developers tend to scrutinize AI-generated code less critically and learn less from it. In contrast, human pairs are more likely to engage in broader discussions and critically examine each other's contributions. The study highlights the limitations of AI assistants in ...
The report proposes initiatives to reduce firearm violence, including using AI and addressing social causes. It also highlights the need for primordial prevention, which involves changing inequitable social structures to prevent violence.
The Center for Computational and AI-enabled Imaging Sciences brings together experts to develop AI-powered medical imaging applications that integrate information from different imaging types. This may include identifying previously unknown early indicators of disease onset.
The new tool, called FSNet, combines machine learning and optimization to find feasible solutions quickly while ensuring constraints are met. It can unravel complex problems several times faster than traditional solvers and even outperform pure machine learning approaches.
A small-scale neuromorphic computer prototype learned patterns and made predictions using fewer training computations than conventional AI systems. The prototype, developed by the University of Texas at Dallas team, integrates memory storage with processing, allowing for more efficient AI operations with lower costs.
The University of Copenhagen has launched a new AI Package to strengthen its research in artificial intelligence and make it more accessible to society. The package aims to bring together various initiatives and research centers across the university to develop AI in a responsible, sustainable, and democratic way.
The SCALE taxonomy introduces a novel framework of educational objectives that align human learning with the emerging needs of the AI age. It encompasses scientific literacy, computational thinking, AI literacy, metacognition, and engineering and design thinking.
Researchers warn that advances in AI and neurotechnology are outpacing our understanding of consciousness, with potential serious ethical consequences. A better understanding of consciousness could have major implications for AI, prenatal policy, animal welfare, medicine, mental health, law, and emerging neurotechnologies.
Researchers from Chiba University developed an AI-based spatial framework to identify areas with limited vertical greening, revealing uneven distribution and high-priority zones for improvement. The framework provides a citywide map of vertical greenery, aiming to enhance cooling, biodiversity, and urban resilience.
Researchers at the University of Surrey have developed a brain-inspired approach to improve artificial neural networks' performance without sacrificing accuracy. The method, called Topographical Sparse Mapping (TSM), rethinks how AI systems are wired by connecting each neuron only to nearby or related ones.
Researchers at UMass Amherst have developed an app that uses Apple Watch data to monitor sleep stages and predict Alzheimer's disease risk. The app, BIDSleep, collects heart rate data and feeds it into an AI model that accurately identifies sleep stages 71% of the time.
A new study from researchers at Dell Medical School found that AI-powered tools can improve patient decision-making and outcomes for knee replacement surgery. Patients who used the digital twin tool reported higher decision quality, less regret, and better knee function compared to those who received educational materials alone.
Researchers have developed an AI-driven facial recognition system called AcroFace that can detect acromegaly by analyzing facial photographs. The system achieved a success rate of 93% in a preliminary trial, outperforming previous attempts. Early detection could lead to timely treatment and improved outcomes for patients with this rare...
Researchers developed an AI-driven framework to predict which plant species are most likely to become invasive before they even arrive in a new location. The methodology combines decades of ecological data with machine learning methods to identify high-risk species.
A new collaboration between Rice University and Baylor College of Medicine aims to predict kidney injury sooner after heart surgery, potentially reducing hospital stays and mortality. The project uses artificial intelligence to analyze vast amounts of data from thousands of cardiac surgery patients.