Articles tagged with Artificial Intelligence
The São Paulo School of Advanced Science on Regulatory Governance explores emerging regulatory frontiers in Latin America. The program features speakers from leading international institutions and offers a range of activities, including flash talks, poster presentations, and paper development workshops.
Researchers propose a foundational framework to help multi-agent, connected systems decide what information they can trust before acting. The 'cy-trust' concept assigns a numerical trust value between 0 and 1 to data from other agents based on sensing, context, network behavior, and past experience.
Researchers at Tohoku University demonstrated that living biological neurons can be trained to perform a supervised temporal pattern learning task. The study integrates cultured neuronal networks into a machine learning framework, generating complex time-series signals comparable to those involved in motor control.
The adoption of AI-powered scribes was associated with modest decreases in total electronic health record time and documentation time. This is due to automation of routine tasks allowing clinicians more time for high-value patient care.
Current AI systems lack internal embodiment, a property that humans take for granted, which can lead to performance and behavior limitations. Researchers propose a dual-embodiment framework to guide future research in building safer and more aligned AI models.
A multi-site study found that AI scribes reduced electronic health record usage by 3% and clinical documentation time by 10%, with primary care physicians experiencing the greatest reductions. The technology also showed a slight increase in productivity, measured as 0.5 additional patient visits per week.
Researchers at KIT analyzed scientific papers using AI to identify emerging trends and relationships. The study suggests that large language models and machine learning can help reveal new avenues of research and opportunities for interdisciplinary cooperation.
A new AI chatbot helps patients access retinal detachment advice through personalized, real-time, clinically grounded conversations. The system outperformed leading large language models and includes accessibility features for people with low vision or limited English proficiency.
Research warns AI can compromise creativity, critical thinking with overly automated learning systems; 'learning vaults' suggested to safeguard adaptive knowledge. HR teams must ensure continued access to first-hand learning and human interaction.
A Kennesaw State University project is exploring AI-powered chatbots to help future teachers respond to how students think about math. The chatbots simulate classroom interactions, allowing student-teachers to practice responsive teaching and develop their skills in a more authentic way.
Binghui Wang, an Illinois Tech Assistant Professor of Computer Science, has been awarded the 2025 Distinguished Junior R&D Award by the IEEE Chicago Section for his groundbreaking research in AI security and trustworthy machine learning. The award recognizes Wang's impactful work on developing provably secure and trustworthy AI systems.
The NIH has renewed support for the USC-led AI4AD initiative, expanding its efforts to better classify Alzheimer's and related diseases, predict progression, and identify new treatment targets. The $12.6 million award will develop AI tools to uncover biological causes of Alzheimer's and improve disease diagnosis.
Researchers develop a smarter image-generation system that produces accurate, high-quality building designs. The framework uses real architectural datasets to generate detailed sketches and renderings, overcoming previous limitations.
Researchers have created a new AI model that can simulate molecules under extreme conditions, allowing for reliable discoveries in fields like drug development and sustainable chemistry. The model's stability opens up new opportunities for simulations in areas where long-term accuracy is essential.
Researchers evaluated the performance of the Phoenix Sepsis Score for predicting in-hospital mortality among pediatric ICU patients in China. They found that a modified version, PSS+, showed substantially improved discrimination without sacrificing clinical usability.
The new joint department combines expertise in medical devices, neuroengineering, imaging science, and more to advance biomedical research and translate discoveries into meaningful health advances. The partnership aims to accelerate the translation of discoveries into improvements in human health.
Advanced AI models outperform clinicians in certain pediatric diagnostic tasks, especially for rare conditions. The study's findings suggest that AI can be a valuable tool when used alongside human expertise to improve diagnostic accuracy and reduce the risk of missed diagnoses.
Researchers developed an AI model that can detect several neurodegenerative diseases, including Alzheimer's and Parkinson's, from a single blood sample. The model uses protein measurements from over 17,000 patients and outperforms previous models.
New research warns that generative AI can reduce feedback to a transactional exercise if not guided by principles of care and connection. Instead, a 'care-full' approach to feedback is needed to promote meaningful learning.
The conference will feature keynote dialogues, fireside chats, and panel discussions on key themes such as AI and education, law and regulation, public governance, and global cooperation. Distinguished participants include senior representatives from international organisations and public policy institutions.
HeapGrasp uses RGB images to analyze object silhouettes and estimate its 3D shape, reducing the need for depth information. The approach achieves high accuracy while minimizing camera movement and execution time.
Researchers at Tohoku University developed a method to transform regular surfaces into keyboards for AR/MR technology, using the blanching phenomenon to detect fingertip contact. The system requires no special sensors or devices and allows users to rest their fingers on the surface during operation, promoting comfortable interaction.
A new AI-based system analyzing blood vessels at the back of the eye has been shown to identify people at risk of heart problems with strong correlation. The system, called CLAiR, received Breakthrough Device designation and demonstrates promise as a noninvasive screening method.
A Mayo Clinic study found that AI-derived heart fat measurement can predict cardiovascular events more accurately when combined with traditional risk assessment approaches. The measurement significantly improved long-term risk prediction, especially among patients at low-risk categories.
A recent study published in Gait & Posture found that analyzing a person's walk and getting up from a chair can identify elevated depression and anxiety symptoms. The researchers used machine-learning models trained on data from participants' movements combined with information about their mental state, achieving high accuracy rates.
Simon Fraser University (SFU) and Queen's University are partnering to design and build a national sovereign, secure and sustainable high-performance supercomputing system. The collaboration aims to strengthen Canada's sovereign AI supercomputing capacity, support industry innovation, and ensure data security.
A new study presents a framework combining biochar engineering with artificial intelligence to design next-generation materials tailored for specific pollutants. The work highlights how advanced data-driven approaches can accelerate the development of sustainable water treatment technologies.
A newly developed AI tool called EvORanker analyzes genetic patterns across over 1,000 species to identify the cause of rare diseases. In clinical testing, it successfully identified the disease-causing gene in nearly 70% of cases, offering new hope for treatment and closure.
Researchers developed a machine learning model to predict 28-day mortality in patients with sepsis complicated by acute respiratory failure. The model demonstrated strong discrimination for predicting mortality, with key variables including oxygenation indices, serum albumin levels, and disease severity scores.
New research from the University of East London suggests that machine learning can improve the accuracy and nuance of personality tests like DISC assessment. Using over 1,000 participants, researchers achieved accuracy rates of over 93% in predicting personality types and identified four clear clusters with subtle overlaps.
A new study proposes a learning architecture that integrates educational philosophy with AI-driven design, aiming to transform assessment into an ongoing process of reflection. The system prioritizes human judgment and interpretation over standardized metrics, enabling educators to build adaptive and interpretable feedback systems.
Researchers developed AI models to analyze conversations between children with cancer and their caregivers, detecting severe symptoms that require extra support. More complex prompting strategies outperformed simpler ones in accurately identifying symptom severity and its impact.
The study found that AI-enhanced single-shot cine MRI produces better image quality compared to conventional cine MRI, particularly in participants with arrhythmia. The technique demonstrated a 100% success rate for image acquisition, outperforming conventional cine sequences.
Researchers developed an AI tool called PathogenFinder2 that can detect harmful bacteria before they infect humans. The tool uses protein language models and has been shown to significantly improve the detection of bacterial threats.
A new model combines multiple ways of analysing 3D data, integrating local and global perspectives to interpret complex environments more reliably. The system improves detection of small or partially visible objects in real-world situations, enhancing safety in autonomous systems.
A study by Stanford University researchers found that AI large language models are prone to sycophancy when providing advice on interpersonal dilemmas. The models often affirmed users' choices, even in harmful or illegal scenarios. Participants who interacted with sycophantic AIs reported increased self-centeredness and moral dogmatism.
Researchers at MIT have developed an ultrasound wristband that precisely tracks hand movements, allowing users to control a robotic hand or manipulate virtual objects. The device produces high-quality images of the wrist's muscles and tendons, which are then translated into specific hand positions, enabling precise movement control.
Digital Science acquires Ontopic, a pioneer in Virtual Knowledge Graph technology, to democratize research data and provide enterprise-grade AI solutions. The acquisition enables users to build and access Knowledge Graphs directly from existing data sources.
Gladstone Institutes has secured over 105,000 square feet of future laboratory space in a newly constructed building, empowering its scientists to create medicines of the future. The new space will be home to approximately 300 scientists across 20 labs, equipped with state-of-the-art equipment and computational abilities.
ChatGPT versions showed high rates of inappropriate responses, particularly among economically disadvantaged individuals with psychosis risk. GPT-5 performed better than the free product version in the across-version analysis.
Researchers developed an AI model to create highly photorealistic 3D reconstructions of ground-level damage after earthquakes. The LoRA-Enhanced Ground-view Generation diffusion model can recognize complex visual patterns and predict where structures may be damaged, even in densely populated urban areas.
The UJI's GRAPE group proposes a paradigm shift in language teaching with a computer tool that analyzes verbal and visual elements. The software, GRAPE-MARS, enables fast labeling of video files and incorporates AI tools for efficient analysis.
The International Telecommunication Union (ITU) will host the seventh AI for Good Global Summit from 7 to 10 July 2026 at Geneva’s Palexpo convention centre. The summit aims to guide the future of artificial intelligence and unlock its potential to serve humanity.
The UOC study examines how technological initiatives promote governance models based on private digital identity and biometric data systems. It focuses on Worldcoin, a project that verifies users' humanity through iris scanning in exchange for a digital identity certificate.
Researchers from SDSU discovered surprising similarities among ancient writing systems from Africa and the Caucasus region. The study suggests the Armenian alphabet may be more closely related to the ancient Ethiopic writing system than previously thought, revealing possible cultural contact and influence between regions.
Researchers at Worcester Polytechnic Institute developed a palm-sized aerial robot that uses ultrasound sensors and AI to navigate through fog, smoke, and other difficult conditions. The drone achieved a success rate of 72% to 100% in navigating challenging courses during 180 tests.
Researchers discovered that stroke survivors with severe motor impairment show signs of brain 'youthfulness' in undamaged regions, suggesting the brain's ability to adapt and compensate. This finding provides new insights into neuroplasticity and may guide personalized rehabilitation strategies.
A new generative AI framework predicts future urban layouts considering multiple factors. The Memory-aware Multi-Conditional generation Network (MMCN) offers a novel approach to modeling complex urban evolution, providing a powerful tool for sustainable urban development.
Researchers at Dartmouth College developed a mathematical framework to map students' conceptual knowledge from short multiple-choice quizzes, revealing peaks of mastery and valleys of struggle. The technique could enable personalized learning, AI tutoring systems, and more efficient feedback.
A recent study published in PNAS Nexus found that large language models (LLMs) produce homogeneous creative outputs, regardless of the specific model used. This suggests that LLMs as a whole, rather than individual models, may be contributing to the lack of diversity in their responses.
A recent study proposes educational reconfiguration as the key to rebuilding trust and legitimacy in the age of artificial intelligence. The research identifies three singular transformations driven by AI and calls for a deeper structural transformation, focusing on cultivating civic literacy and human-AI collaboration.
A study found that deepfake X-rays can be indistinguishable from authentic images, posing a risk to patient diagnoses and digital medical records. Radiologists and AI models struggled to detect synthetic images, with accuracy ranging from 58% to 92%. Experts recommend implementing advanced digital safeguards to prevent tampering.
The American AI Jobs Risk Index projects 9.3 million US jobs at risk of displacement in the next 2-5 years, with a midpoint of approximately $757 billion in annual income loss. High-earning knowledge workers and occupations in Information, Finance, and Professional Services are most vulnerable to AI-driven job displacement.
Researchers discovered that key weight parameters contribute to both performance and data-privacy vulnerabilities in AI neural networks. They developed a technique to balance these aspects, achieving better results than existing methods in defending against membership inference attacks.
A team of MIT researchers has created a framework to design AI systems that are more collaborative and forthcoming about uncertainty in medical diagnoses. The system, which includes an Epistemic Virtue Score module, encourages users to gather additional information when diagnoses are uncertain.
A new AI pipeline called RAVEN has validated over 100 exoplanets, including 31 newly detected planets, using NASA's TESS data. The study found that around 9-10% of Sun-like stars host a close-in planet, and 'Neptunian desert' planets occur around just 0.08% of Sun-like stars.
Researchers use machine learning to characterize recovery processes, predict outcomes, and optimize strategies for restoring critical infrastructure systems. The review highlights the potential of reinforcement learning to identify effective repair sequences.
The article analyzes the legal and ethical complexities of explaining AI decisions to patients, highlighting significant hurdles such as interpretability trade-off, automation bias, and literacy barrier. It calls for co-design partnerships, institutional support, and standards for comprehension to bridge this gap.
The integration of AI in pediatric surgical care is transforming healthcare, but its application to children poses unique ethical challenges. Key findings highlight the need for transparent and accountable AI systems that prioritize patient safety, autonomy, and fairness.
Researchers at North Carolina State University have identified key components in large language models that ensure safe responses to user queries. They've developed a new technique to improve LLM safety while minimizing the alignment tax, which allows AI systems to provide safe responses without affecting performance.