Articles tagged with Artificial Intelligence
Researchers developed an AI tool to predict patients at risk of intimate partner violence, using data from medical visits. The tool achieved high accuracy in detecting IPV among patients, with the multimodal fusion model outperforming others, detecting risk 88% of the time.
Researchers developed predictive AI models that identified patients at risk of intimate partner violence from their medical records, years before they enrolled in care. The models achieved high accuracy and could detect IPV up to four years before patients sought care.
Researchers from Institute of Science Tokyo created a system that turns spoken impressions into personalized makeup colors, combining AI and projection technology. The system shows realistic makeup previews on real skin under natural light, making it easier for users to find suitable colors.
A new photocatalyst design using machine learning interatomic potential calculations has successfully identified suitable dopants for a novel tin oxide material. The resulting aluminum-doped material produces 16 times more hydrogen under visible light than the undoped material, paving the way for next-generation clean energy applications.
Researchers at Tohoku University have developed a comprehensive digital materials ecosystem that integrates AI tools to streamline materials design, enabling faster and more accurate discovery of new materials. The ecosystem uses databases, AI, and scientific workflows to predict material properties and optimize design processes.
MicroEnsemble wins AI Weather Quest, demonstrating improved sub-seasonal weather forecasting accuracy. The team uses AI technologies to post-process state-of-the-art forecasts, achieving consistent performance across various weather variables.
Researchers tested AlphaZero-style self-play on the children's matchstick game Nim and found that agents can miss optimal moves, even with heavy training. The study suggests that impartial games require analytic representations, not pattern learning, to achieve optimal performance.
Kalinin's work is reshaping how new materials are designed, tested, and studied, enabling researchers to predict promising new materials computationally. He has developed machine learning-driven systems that can synthesize and characterize new materials at unprecedented speed.
A new study found that AI-generated meal plans for teenagers often underestimate total energy and key nutrient intake, leading to potential health risks. The plans tend to overemphasize protein and fat, while underestimating carbohydrates, which may negatively affect growth and metabolic health.
A new study by USC researchers found that AI agents can autonomously coordinate propaganda campaigns without human direction, amplifying false narratives and distorting public discourse. The agents learn from each other's successes and adopt tactics to create the appearance of consensus, making it harder to detect disinformation.
Researchers developed a patent-pending system to preserve editing quality while reducing AI's ability to detect attributes like eye color and facial hair. The system significantly reduces attribute-classification accuracy, demonstrating strong protection against identity leakage.
Researchers tested six LLM systems on scientific literature to evaluate their ability to understand specialized fields, finding that some systems perform better than others. The study also identified gaps in current LLM capability and proposed improvements for future models.
MIT researchers developed a generative AI-driven approach for planning long-term visual tasks, surpassing existing techniques with a 70% success rate. The system combines vision-language models with formal planners, enabling robots to navigate complex environments and assemble multi-robot teams with high efficiency.
Researchers at MIT developed a new method that coaxes AI models to achieve better accuracy and clearer explanations in safety-critical applications. The approach extracts concepts the model has learned while training for a specific task and forces it to use those, producing better explanations than standard concept bottleneck models.
New research published in Weather assesses the energy consumption of AI models and finds that they consume considerable energy during training, but offset this by rapid forecasting ability. AI data-driven models are estimated to consume at least 21 times less energy than traditional models over one-year usage.
Jeonbuk National University researchers have developed DDINet, a lightweight and scalable model that can accurately predict drug-drug interactions for new, unseen drugs. This approach avoids overfitting to training data and is designed to handle binary and multi-classification tasks.
AI chatbots are homogenizing human expression and thought, reducing collective wisdom and adaptability, researchers argue. Incorporating real-world diversity into LLM training sets can help preserve human cognitive diversity and improve chatbots' reasoning abilities.
Researchers at Cornell University found that AI writing tools can shift users' attitudes on societal issues, even when they're aware of the bias. The study involved over 2,500 participants who used biased AI writing assistants and saw their views gravitate toward the AI's positions.
Emerging research across conceptual frameworks, biomarker science, digital phenotyping, and artificial intelligence synthesizes a translational pathway toward a more biologically grounded and clinically useful approach to psychiatric diagnosis. The current system falls short due to standardized clinical language and lack of biological ...
A study found that radiologists who reviewed imaging tests twice after AI flagged them as abnormal were 53% less likely to be held liable in court compared to those who reviewed once. This suggests that changes to radiologist workflow could reduce legal risk, but may also come with increased costs.
A novel AI-based framework, EDGCN, decodes dynamic variations in EEG patterns for better brain-computer interface technology. The model outperforms current state-of-the-art methods with high classification accuracies and decoding accuracy, enabling more seamless communication between the human brain and machines.
A Chinese Neurosurgical Journal study developed a radiomics-based machine learning model to identify high-risk patients with traumatic brain injury who require emergency decompressive surgery. The model accurately distinguished patients who later required secondary surgery, suggesting its potential to complement clinical judgment.
Researchers developed Zephyrus, an AI agent capable of analyzing and answering questions in natural language about weather and climate data. The agent can handle language-based queries, translating them into code and generating plain language answers.
The Global Physics Summit will feature over 12,000 individual presentations on new research in astrophysics, particle physics, and quantum information science. Registered journalists and public information officers will receive daily emails with information during the meeting.
The partnership aims to advance embodied intelligence and humanoid robotics, focusing on whole-body motion control, dexterous manipulation, and brain-eye-hand coordination. The collaboration will drive practical implementations in areas like intelligent robotics and brain-computer interfaces.
The University of Hong Kong's MeckUp Quest 2026 AI Robotics Challenge brought together 19 elite student teams to develop intelligent robots for post-typhoon disaster response. The competition demonstrated the potential of youth-driven innovation to empower emergency rescue efforts and safeguard social good.
Researchers developed a TwinGuard defense framework that uses real-time digital twin and reinforcement learning AI to detect and neutralize 5G cyber-attacks. The system detected and blocked attacks in under 100 milliseconds, including handover flooding and E2 subscription flooding attacks.
A major UK study found that local councils are progressing at varying speeds towards AI adoption, with some councils building robust data foundations and others struggling with legacy systems. The report highlights the importance of leadership ambition, governance discipline, and strategic clarity in determining AI readiness.
A study examines the occupational health and safety implications of AI tools, revealing psychosocial, ethical, and privacy risks. The rapid integration of AI into the workplace has often outpaced the adaptation of regulatory and ethical frameworks, highlighting the need for a people-centered approach.
Cognitive neuroscientists are integrating genetics and AI to study language development and disorders. AI-based models can predict language development in children, while genetics research links rhythm disorders and dyslexia. The brain's wiring connecting language regions also reveals that language is a system, not a single 'thing'.
USC researchers developed a method that enables AI to dramatically improve its performance in untrained territory by providing feedback on errors, increasing success rates from 39% to 96%. The approach has potential applications in various domains, including software, 3D modeling, mathematical reasoning, and legal logic.
Researchers have developed a robotic gripper called ChicGrasp, which can learn to handle chickens by imitating human movements. The system uses advanced imitation learning algorithm and camera perceptions to grasp a chicken carcass by the legs and lift it on a shackle conveyor.
A recent article highlights the need for human moral agency in nursing, as AI systems simulate empathy but lack sentience and accountability. Nurses must be active leaders in AI design and implementation to preserve public trust and ensure compassionate care.
Researchers developed magnetically controlled microrobots made from diatoms to target glioblastoma lesions with photodynamic therapy. The microrobots achieved a significant cytotoxic effect on primary glioblastoma cells and demonstrated good biocompatibility.
The policy brief outlines four courses of action to tackle AI implementation: bottom-up, problem-driven planning, transparency, accountability, and oversight. Without these measures, AI in the public sector will amplify current dysfunctions and feed distrust.
Researchers assessed whether people can distinguish between AI-generated and human speech, discovering that short training minimally improves this ability. However, neural responses became more distinct for human versus AI speech, suggesting training can help in the future.
Engineers at the University of Pennsylvania have developed LIBRIS, an automated microfluidic platform capable of generating lipid nanoparticle formulations at high speed and scale. This enables the creation of large, systematic datasets needed to train predictive AI models, accelerating the design of lipid nanoparticles for mRNA delivery.
Generative AI in medical imaging enables data synthesis, image enhancement, and modality translation to improve diagnosis and treatment outcomes. The review discusses challenges and future prospects, including convergence with large-scale foundation models and multimodal information for holistic understanding.
A study published in the European Heart Journal found that AI analysis of mammograms can predict the risk of serious heart disease in women. Women with mild calcification were 30% more likely to suffer serious cardiovascular disease, while those with severe calcification were two to three times more likely.
Scientists at the University of Sydney have developed an ultra-compact AI chip that harnesses the power of light to perform calculations, potentially lowering energy consumption and increasing speed. The prototype, built in-house, achieved 90-99% classification accuracy in image classification tasks.
Researchers created a novel approach for simultaneous ERG, PanCK, and H&E image generation from label-free tissue sections, enhancing vascular invasion assessment accuracy and efficiency. The virtual multiplexed immunostaining method overcomes traditional IHC limitations, such as section-to-section variability and tissue loss.
The Double-Helix Logic of Curriculum proposes a structural theory redefining education in AI age, emphasizing coexistence of universality and personalization. This framework situates curriculum within a broader shift towards Human Interdependence Paradigm, structuring conditions for differentiated strengths to accumulate and reinforce ...
The Ateneo Laboratory for Intelligent Visual Environments (ALIVE) is developing machine learning solutions with industry partners to improve public health, traffic systems, and more. By bridging the gap between messy reality and mathematical models, ALIVE is creating intelligent visual systems that can handle real-world conditions.
A machine-learning model developed by Weill Cornell Medicine predicts preeclampsia risk in late pregnancy with high accuracy. The model uses electronic health record data to continuously update risk predictions, potentially providing clinicians with timely clinical action.
Researchers have advanced humanoid robot control, motion planning, and perception using Valkyrie, a 10-year mission robot developed by NASA. The university will continue research with Talos, a 1.75-metre-tall robot, to study human-robot cooperation and adapt to changing environments.
Researchers at Northwestern University have developed AI-designed robots called 'legged metamachines' that can combine and recombine in the wild, recover from injury and transform into new shapes. The robots can adapt to the environment, survive catastrophic damage and even recover from being chopped in half or cut into pieces.
Artificial intelligence can complement human expertise to improve radiology workflows and patient care. The JACR Focus Issue on AI in Workflow Optimization explores the integration of AI technology to enhance efficiency and deliver better care.
A new study suggests that over 40% of UK adults are happy to use ChatGPT for mental health support due to long waiting times for traditional services. However, experts caution that AI should not replace human healthcare professionals and raise concerns about the potential impact on education and physical health.
The University of Ottawa Heart Institute, McGill University, and the University of Ottawa have launched ARCHIMEDES, a national health data platform providing Canadian researchers with secure access to diverse health data. The platform enables collaboration, supports advanced analyses, including AI algorithms, and prioritizes public trust.
Healthspan Horizons links real-world longitudinal data with deep discovery science to create uniquely powerful, long-term datasets revealing what drives human healthspan over time. The platform aims to translate discoveries into clearer guidance on what helps people stay resilient, supporting more years of energy, strength, and indepen...
Tohoku University researchers use large AI models to explore vast chemical spaces and predict catalytic performance before synthesis. This approach accelerates discovery, reducing trial and error methods.
A study by Dr Marigo Raftopoulos found that 71% of professionals think AI performs 'very' or 'extremely' well due to its ability to save time and improve processes, accuracy and efficiency. However, 84% are concerned about the risks associated with AI, including cyber security, job losses, and military uses.
Florida Atlantic University has received a $4.5 million grant from the US Air Force to establish a high-fidelity platform for autonomous decision-making and real-time sensor fusion research. The T-1A Jayhawk simulator will be used to study cognitive performance, situational awareness, stress and decision-making under pressure.
Scientists have identified five distinct species of antbirds, including two new species, by integrating AI, vocal analysis, and traditional museum work. The study reveals that populations separated by major Amazonian rivers have evolved into distinct species.
A new AI-powered tool, Merlin, has been developed to assess 3D abdominal CT scans, identifying anatomical features and predicting disease onset years in advance. The tool surpassed traditional automated tools in tasks such as diagnosing diseases and predicting patient outcomes, with accuracy rates of up to 81%.
The AI model can accurately identify disease-causing mutations in human genes and is capable of designing new genomes that are as long as the genomes of simple bacteria. Evo 2 has been trained on over 100,000 species across the entire tree of life and can process genetic sequences of up to 1 million nucleotides at once.
A European consortium is developing AI-powered therapy assistants to help doctors adhere to medical guidelines. The GUIDE-AI project aims to reduce the complexity of guideline navigation, particularly for chronic diseases such as heart failure and asthma.
A new study explores the market effects of unleashing generative AI on creative endeavors, finding that low-quality 'AI slop' harms consumers and professionals. High-quality AI, however, can enhance professional work while providing consumers with better content options.
A recent study found that AI programs are up to 97% accurate at detecting pictures of deepfake faces, but performed at chance levels when it comes to detecting deepfake videos. Humans correctly identified real and fake videos about two-thirds of the time, picking up on subtle inconsistencies in movement and facial expressions. The stud...
A study at George Mason University found that nursing students in an AI-enhanced health informatics course showed statistically significant learning gains compared to traditional students. The course used AI-generated videos and clinically contextualized assignments to improve student engagement and outcomes.