Articles tagged with Artificial Intelligence
Researchers synthesized AI-driven analysis of protein structures to predict drug interactions and optimize synergy. This fusion approach aims to solve drug resistance, reduce side effects, and guide personalized treatment in precision medicine.
Large language models are revolutionizing bioinformatics research by decoding complex biological data, predicting protein structures, and identifying disease-linked genes. The models' five core strengths include enabling breakthroughs in protein analysis, drug discovery, and genomic research.
Researchers from Shibaura Institute of Technology used machine learning algorithms to predict bearing layer depth and assess liquefaction risk. The study found that random forest models outperformed others, especially with increasing spatial data density.
Researchers found that large language models like ChatGPT outperform humans in identifying neuromyths, but struggle to critically correct them. By prompting AI to explicitly correct false assumptions, error rates can be significantly reduced.
Researchers developed a machine learning model to predict liquid crystalline polyimides with high thermal conductivity, achieving 96% accuracy. The model identified six promising candidates, which demonstrated up to 1.26 W/mK thermal conductivities, accelerating the development of efficient thermal materials.
Researchers are developing an AI imaging approach to distinguish between tumor progression and treatment effects in glioblastoma patients, promising to improve care and outcomes. The AI approach has already shown accuracy rates of up to 74% in initial testing.
A new study combines eye tracking and artificial intelligence to identify key moments in educational videos that matter for learning. The research provides promise for individualized video education, where AI dynamically adjusts the content for a student's understanding.
Researchers developed a new speculative decoding framework called SPECTRA, which optimizes internal and external speculation for accelerated text generation. By integrating a core module and an optional retrieval module, SPECTRA achieves substantial speedups across diverse tasks and model architectures while preserving output quality.
A large-scale modeling study led by MIT researchers reveals that dynamically adjusting vehicle speeds can cut annual city-wide intersection carbon emissions by 11-22%. Implementing eco-driving measures could also result in a 25-50% reduction in CO2 emissions if only 10% of vehicles adopt the technology.
A new principle for AI regulation, multiplicity, aims to design systems that expose users to a broader range of narratives, support diverse alternatives, and encourage critical engagement. This can help enrich the human experience and prevent the suppression of diverse perspectives, cultural diversity, and social tolerance.
Researchers developed a deep learning model to predict ADRs using molecular structures, achieving high accuracy in detecting rare effects and minimal risk for novel molecules. The model demonstrated potential as a decision-support tool in early-phase drug discovery and regulatory safety monitoring.
Researchers found that AI-augmented, personalized feedback from human instructors enhanced surgical performance and risk management. This study suggests the integration of human expertise in AI-driven education can improve engagement and ensure contextualized, adaptive feedback.
A University of Washington study found that biased AI chatbots can influence users' political views, with those who interacted with the most biased systems shifting their views more significantly. The researchers suggest education about AI may help mitigate this effect.
A study by researchers at UC San Diego and University of Chicago found that most visual artists lack the technical know-how to block AI crawlers, despite wanting to protect their creative work. The team developed a tool called Glaze, but more effective solutions are needed to prevent AI data harvesting.
A new deep learning model, MSI-SEER, achieves high accuracy in predicting microsatellite instability-high tumors and their responsiveness to immunotherapy. The model combines tumor MSI status with stroma-to-tumor ratio for highly accurate ICI response prediction.
The system tracks and analyzes crop development using data from sensors, biosensors, the Internet of Things, and AI. Strong security protocols ensure farmer data remains private and resilient against future quantum computer attacks. The research team plans to improve their system with faster sensor processing and a solar-powered battery.
The Ateneo de Manila University's Business Insights Laboratory explores how AI can turn handwritten sales logs into manageable digital data. The system uses OCR and LLM technology to recognize products, match prices, and tabulate sales summaries, helping businesses quickly identify bestsellers or slow-moving stock.
Researchers developed a novel framework using generative AI and musculoskeletal simulation to create synthetic gait data. This approach enables more robust and generalizable gait analysis across various patient populations and clinical environments.
Researchers have developed a novel synthetic ligand using AI-based computational protein design, activating the Notch signaling pathway in T-cells. This breakthrough enables broadened clinical T-cell production and advances immunotherapy development.
Researchers at NJIT used artificial intelligence to discover new porous materials capable of revolutionizing multivalent-ion batteries. The AI-driven approach uncovered five entirely new materials with large, open channels ideal for moving bulky multivalent ions quickly and safely. These findings offer a promising solution for the futu...
Physicists used a machine-learning method to identify surprising new twists on the non-reciprocal forces governing a many-body system. The AI approach provides precise approximations for these forces, correcting common theoretical assumptions with an accuracy of over 99%.
The article provides reporting recommendations for studies evaluating chatbot health advice, highlighting the importance of standardization in interpretability. Key findings include the need for clear guidance on chatbot performance metrics and data quality control.
Hollings researchers use natural language processing to identify primary cancer types in medical notes, improving treatment accuracy and personalized care. The AI model achieves high accuracy rates, outperforming standard medical codes in identifying primary cancer diagnoses.
A new AI tool can learn to read medical images with far less data, cutting down the amount of required data by up to 20 times. The tool improves upon medical image segmentation, a labor-intensive task often performed by experts, and boosts model performance in settings with limited annotated data.
The survey highlights a dual-path framework for harnessing large language models' intrinsic reasoning and integrating them with external methodologies. Large language models are employed for step-by-step reasoning methods, enabling the decomposition of complex tasks and exploration of multiple reasoning paths.
A technology expert predicts AI-based medicine will revolutionise care for Alzheimer’s and diabetes, but access must be made available to all patients. The author highlights key innovations such as diagnostic imaging and surgical robots that will aid in more accurate diagnoses and treatments.
The ERIC system combines doorbell cameras and AI to analyze rainfall estimation and automatically adjusts irrigation schedules for more precise water use. Researchers estimate users can save up to $29/month in utility costs and 9,000 gallons of water per month with the innovative irrigation system.
Researchers used ChatGPT to assess patient eligibility for clinical trials, identifying suitable candidates within minutes. The AI was more accurate than GPT-3.5 but slightly slower and more expensive.
A research team developed an innovative unsupervised model for industrial anomaly detection using paired well-lit and low-light images. The model leverages feature maps, Low-pass Feature Enhancement, and Illumination-aware Feature Enhancement to detect anomalies while remaining lightweight and memory-efficient.
A new study by MIT researchers introduces a method for machine learning with symmetry that is provably efficient, clarifying a foundational question in the field. This approach could lead to more powerful AI models designed to handle symmetry, benefiting applications such as drug discovery and materials science.
Researchers at Salk Institute launched a machine learning framework called ShortStop to explore overlooked DNA regions and discover microproteins with potential roles in disease. The tool identified 210 new microprotein candidates in lung cancer data, including one validated target for therapeutic treatment.
Researchers argue that successful AI policy requires scientific understanding and systematic analysis to inform decision-making. The authors propose mechanisms to incentivize rigorous model evaluations, increase transparency, and monitor post-deployment harms to achieve evidence-based governance.
Researchers at Cornell University have developed a way to watermark light in videos, allowing fact-checkers to detect if video footage is fake or has been manipulated. The technique involves hiding information in nearly-invisible fluctuations of lighting at important events and locations, which can be used to reveal any malicious editing.
Recent AI-based models, such as Asian Prostate Cancer Artificial Intelligence and Galen Prostate, optimize screening and reduce unnecessary biopsies. These models use multimodal clinical parameters and convolutional neural networks to detect prostate cancer and identify aggressiveness of cancer cells.
The journal is seeking high-quality submissions that explore the potential of voice and speech analysis in diverse medical applications. Submissions focus on acoustic and voice analysis techniques, voice or vocal phenotyping, and vocal biomarkers in health and medicine.
The FDA has authorized artificial intelligence devices for Alzheimer's and related dementias despite limited demographic data. Disease status, age, sex, and race were reported for fewer than half of devices, raising concerns about real-world generalizability.
The new book highlights the transformative role of artificial intelligence (AI) and machine learning (ML) across various domains, including mechatronics, cybersecurity, digital health, and automation. Readers will gain practical insights into AI-based techniques in power systems, social media management, and healthcare diagnostics.
A new study reveals that large language models can generate convincing but biased peer reviews, nearly indistinguishable from human writing. The AI excelled at generating persuasive rejection comments and fabricating reasons to cite unrelated studies, posing a serious risk to the integrity of scientific publishing.
Gemini achieved highest accuracy, followed by Claude-3-opus and GPT, with accuracy varying significantly across single-image tasks and continuous slices. Simplified prompts improved diagnostic performance, but Gemini and GPT struggled with normal image recognition.
This book provides a beginner-friendly resource on the impact and evolution of decentralized networks, highlighting their applications in healthcare, supply chains, agriculture, climate monitoring, and education. The authors emphasize sustainability, data security, and ethical tech adoption.
Brown University researchers will lead a national institute focused on developing AI assistants capable of trustworthy, sensitive and context-aware interactions with people. The goal is to create AI systems that can interpret a person's unique behavioral needs and provide helpful feedback in real time.
Experts suggest a clear separation of roles between AI systems and radiologists to overcome challenges such as cognitive biases and misaligned incentives. A framework for role separation is proposed, including three models that can be adapted to specific needs, enabling institutions to implement hybrid approaches.
The Idaho National Laboratory is collaborating with Amazon Web Services to develop artificial intelligence tools for nuclear energy projects. The laboratory aims to reduce the costs and timeframes of designing, licensing, building, and operating nuclear facilities using advanced AI technologies.
The National Science Foundation has awarded the University of California, Davis a $5 million grant to develop an Artificial Intelligence Institutes Virtual Organization (AIVO), a community hub for AI research and education. The new hub will provide events, venues, and partnerships to promote cross-institute connections and foster posit...
Researchers developed a personalized brain stimulation system powered by AI to safely enhance concentration from home. The system, which uses non-invasive brain stimulation and adaptive AI, showed significant improvements in sustained attention and learning abilities.
Researchers used AI tools to analyze posts from 54 Reddit communities, finding similarities in speech patterns between hate speech groups and communities for certain psychiatric disorders. The study suggests that exposing oneself to hate speech online may lead to decreased empathy and traits similar to Cluster B personality disorders.
Researchers developed drones that can safely explore air ducts by understanding airflow patterns and stabilizing their position using lasers and AI. The study opens up new application domains for drones in industrial inspection and public safety.
The article highlights the risks of deepfakes and manipulated media, with expert Hany Farid advocating for legal requirements, imperceptible watermarks, and digital media literacy to mitigate these harms. He also explores policy landscape and authentication techniques to sort real from synthetic content.
Researchers at Chan Zuckerberg Biohub San Francisco and Stanford University have developed an AI-driven Virtual Lab that can tackle sophisticated scientific problems. The team of AI agents, each equipped with varied expertise, can formulate, refine, and carry out complex research strategies, producing results that can be validated in r...
Researchers create a technique to model organic objects, creating photorealistic 3D images of complex shapes for autonomous sorting. This method improves robotic systems that sort microscopic marine fossils used in climate research.
A new AI algorithm has been shown to detect nearly one-third of interval breast cancers missed at screening using digital breast tomosynthesis (DBT), according to a study published in Radiology. The algorithm improved the performance of DBT, reducing interval cancers.
Researchers at Stanford Medicine have developed a virtual lab powered by AI, allowing them to tackle complex biological problems and devise innovative solutions. The virtual lab has been successfully tested in devising a new basis for a vaccine against recent COVID-19 variants using nanobodies.
Lydia Kavraki, a pioneering researcher in robotics and AI, has been elected to the European Academy of Sciences. Her work on physical AI, transforming robotics and biomedicine, and leadership at Rice University's Ken Kennedy Institute have earned her this prestigious honor.
Six UNH faculty members receive prestigious CAREER grants to develop innovative technologies addressing pressing global issues. The recipients' projects focus on climate resilience, artificial intelligence, software reliability, and healthcare technologies, aiming to improve public safety and well-being.
Researchers at the University of Rochester are developing biologically inspired predictive coding networks for digital image recognition using analog circuits, which could lead to more efficient drones. The team aims to approach the performance of existing digital approaches and translate it to complex perception tasks needed by self-d...
Researchers at NTU Singapore have developed an AI-powered robotic system to assemble cyborg insects for search and rescue efforts, significantly reducing preparation time and human error. The automated system can attach electronic backpacks to Madagascar hissing cockroaches in under 8 minutes, about 30 times quicker than manual methods.
A team of computer scientists created 2,300 original sudoku puzzles and asked AI tools like OpenAI's ChatGPT to solve them. The results showed that while some AI models could solve easy sudokus, most struggled to provide accurate explanations, raising questions about the trustworthiness of AI-generated information.
A new blood test leverages AI to identify Lyme disease sooner and more accurately than current standard tests, with sensitivity and specificity over 90%. Additionally, an AI tool, Medicine-GPT, demonstrates promise in helping adolescents gather useful medical information, outperforming ChatGPT-4 on completeness and helpfulness.
New research highlights the transformative power of digital technology in shaping the e-patient era, enabling active participation and co-production in healthcare. Key technological innovations have built upon each other, providing patients with unprecedented tools for self-care and interaction with the healthcare system.
A machine learning model accurately predicted no-shows and late cancellations in primary care practices, with the gradient boost model achieving 85% accuracy for no-shows and 92% for late cancellations. Patient characteristics such as sex, age, and socioeconomic status played a significant role in missed appointments.