Articles tagged with Artificial Intelligence
The 13th Annual Meeting on Skin Challenges 2022 will address key skin ageing and challenges such as skin microbiome, cancer, inflammation, wound healing, and more. The event will feature experts from industry and academia presenting their research on these topics.
A new AI framework, M2YOLOF, has been proposed for fast and high-precision target detection. The framework reduces model complexity and inference time overhead by using a multi-input single-output approach.
A Mayo Clinic study found that clinicians who were high adopters of an AI-enabled clinical decision support tool were twice as likely to diagnose low left ventricular ejection fraction as those who were low adopters. The tool's accuracy was critical in detecting serious health conditions before symptoms appeared.
Researchers can now study microplankton at an individual level using holographic microscopy and AI, gaining a deeper understanding of their movement, growth, reproduction, and interactions. This breakthrough provides new insights into the ocean's oxygen production and carbon cycle.
The US Department of Energy's Oak Ridge National Laboratory has developed a massive geographic dataset, USA Structures, using deep learning to forecast potential damage and accelerate emergency response. The dataset provides critical information on building outlines and attributes, enabling FEMA to prioritize response efforts.
Researchers used AI to predict compounds that could neutralize reactive oxygen species causing baldness. They successfully regenerated hair on mice using microneedle patches, providing a promising new treatment for androgenic alopecia.
A study of 9,303 US adults found that people with cardiovascular disease were less likely to use wearable devices, highlighting disparities in access and use. The research emphasizes the need to ensure equitable access to wearables to benefit high-risk populations.
Researchers at the University of Illinois developed an AI-powered system that uses a molecule-making machine to find optimal reaction conditions for synthesizing chemicals. The system doubled the average yield of a challenging class of reactions, paving the way for faster innovation and automation in biomedical and materials research.
Researchers aim to identify electrical activity in brain consolidation and test targeted brain stimulation to strengthen memories. The study could advance understanding of neural mechanisms involved in memory formation, potentially benefiting people with Alzheimer's disease.
An AI-based model has been developed to assist radiologists in detecting and identifying leadless implanted electronic devices (LLIEDs) on chest X-ray images. The model achieved high detection and classification accuracy, even with suboptimal image quality, and showed promise for real-world deployment.
Researchers at Purdue University and the University of Tennessee, Knoxville, have developed a metamaterial that can learn to adapt to its surroundings on its own. The material uses shape to store information in microseconds, allowing drones to quickly recall patterns associated with dangerous conditions.
The Beckman Institute has established a new national collaborative Biomedical Technology Research Resource to develop label-free optical imaging technologies. The center aims to create optical and computational imaging technologies that can serve as a resource for clinicians and researchers.
Researchers aim to use quantum computer-based AI to accelerate drug discovery, cutting costs and time by exponentially increasing processing power for complex problems. Quantum AI models have higher capability to approximate desired functionality compared to classical neural networks.
A study by Professor Dennis Murphy Odo found that automatic text simplification (ATS) software makes authentic materials more comprehensible for L2 learners with high reading proficiency. However, the tool's effectiveness is limited for learners with lower reading proficiencies. The researcher suggests that ATS tools need to be further...
Lei Yang is conducting research on developing fair AI-assisted mobile dermatology diagnosis technology using unsupervised federated learning. The goal is to ensure participation from all socioeconomic populations and find the best network while considering hardware constraints.
A new study published in iScience found that humans tend to follow a two-step process when in a crowd, with an initial reflexive imitation followed by a more deliberate strategic decision. The research suggests this influence affects not only social norms but also immediate actions and underlies behaviors such as rioting and mass panic.
Researchers developed an AI-based model that combines artificial intelligence and weather forecast models to predict extreme wildfire danger with high accuracy. The new method can produce forecasts of extreme fire danger out to one week at finer scales (4km x 4km resolution), increasing its utility for fire suppression and management.
Researchers at MIT have developed a new method that uses optics to accelerate machine-learning computations on low-power devices. By encoding model components onto light waves, data can be transmitted rapidly and computations performed quickly, leading to over a hundredfold improvement in energy efficiency.
Researchers at Indiana University have developed a virtual reality therapy to aid in substance use disorder recovery, using 'future-self avatars' to help people choose long-term rewards over immediate gratification. The technology has shown promising results in lowering relapse rates and increasing future self-connectedness.
FathomNet aggregates images from multiple sources to create a publicly available, expertly curated underwater image training database. The database uses artificial intelligence and machine learning to alleviate the bottleneck for analyzing underwater imagery, accelerating important research around ocean health.
A new AI model developed by researchers at the University of Georgia uses artificial intelligence to analyze terabytes of quail call recordings, allowing wildlife managers to gather data in a matter of minutes. The model is accurate, picking up between 80% and 100% of all calls, even in noisy recordings.
Omnipose, a deep learning software, can identify various types of tiny objects in micrographs with high precision, including bacteria of all shapes and sizes. It overcomes limitations of previous approaches by handling object overlap and detecting cell intoxication, making it a game-changer for biological image analysis.
A study found that Italian regions with high levels of environmental pollution have higher cancer mortality rates, even after controlling for lifestyle factors. The analysis identified specific sources of pollution associated with certain types of cancer, highlighting the need for environmental reduction and prevention to combat cancer.
A new study aims to diagnose severe neurodegenerative diseases like ALS and FTD with the help of speech tests. AI can analyze subtle nuances of speech patterns, including pauses, speed, and melodic aspects, to detect early changes.
A new project will develop a technique to quantify uncertainty in AI-based tools used for image analysis and create a questionnaire to assess patients' risk tolerance when using these tools. The goal is to ensure that AI-assisted clinical decisions are informed by the inherent uncertainty of imaging technologies.
Researchers found that AI language models exhibit significant implicit bias against individuals with disabilities, resulting in inaccurate sentiment analysis and potential misclassification of posts as toxic. The models' biases were apparent in various applications, including autocorrect and social media content moderation.
A team of researchers at the University of Pittsburgh is using artificial intelligence to create more efficient and effective organoids. By designing and printing smarter cell structures that mimic human organs, they aim to reduce trial-and-error methods and costs, ultimately contributing to new methods in disease research and human he...
Researchers created living brain cells in a dish that can perform goal-directed tasks and adapt to changes, opening doors for new understanding of brain function. The study also raises possibilities for alternative animal testing and biomimetic research.
Researchers from McGill University and MIT developed an AI system that can learn the rules and patterns of human languages on its own. The model automatically generates higher-level language patterns that can be applied to different languages, achieving better results.
A perspective paper explores the role of clinician-data-scientists in healthcare, emphasizing their need for interdisciplinary knowledge and training. The researchers highlight the importance of integrating data science into conventional medical education to prepare clinicians for the digital health era.
Researchers at North Carolina State University developed a blueprint for incorporating ethical guidelines into AI decision-making programs. The new mathematical formula, based on the Agent, Deed, and Consequence (ADC) Model, considers intent, character, and consequences of actions to make more informed decisions.
A Cleveland Clinic-led research team created a discovery tool outlining interactions between COVID-19 and host proteins, identifying potential host-targeting therapies. The study confirmed over 200 interactions and discovered new ones, highlighting promising approaches for treating COVID-19.
New research highlights the dangers of AI-powered recruitment tools that claim to remove discrimination from hiring. The tools reduce race and gender to trivial data points and often rely on personality analysis that is
The program cuts the average time it took for new patients to get their first treatment from 97 days to just 41 days, while also allowing more than 1,000 new patients to begin treatments. The system has made significant improvements in efficiency and patient satisfaction, with appointment no-shows dropping by 50%.
A University at Buffalo-led research team has been awarded a $5 million grant to develop digital tools that can help older adults recognize and protect themselves from online deceptions. The project, DART, aims to reduce online fraud among older adults, who lose billions of dollars each year due to scams.
A new screening test App can produce accurate results using just people's voice recordings, improving the management of Parkinson's disease and severe COVID-19. The smartphone App takes just 10 seconds to reveal whether a person may have Parkinson’s disease and should be referred to a neurologist.
Researchers developed a new technique that enables on-device training using less than a quarter of a megabyte of memory, reducing the need for powerful computers and central servers. This approach preserves privacy by keeping data on the device, making deep learning more accessible for low-power edge devices.
A new study proposes a powerful computer-modeling approach to cell simulations, reaching unprecedented simulation timescales at all-atom resolution. The technique combines advantages of protein docking and molecular simulations, enabling faster and more precise treatment of human disease.
The new computer chip uses a transistor-free design that eliminates data transfer time and minimizes energy consumption. It offers up to 100 times faster performance than conventional computing architectures, making it ideal for AI applications.
A Rutgers-led study statistically evaluated government data to determine if COVID-19 vaccination increases the incidence of Guillain-Barré syndrome. The results show no significant association between any COVID-19 vaccinations and the disease.
Physicists at the University of Basel have developed a computational shortcut for neural networks, allowing for faster calculation of optimal solutions without training. This breakthrough provides insight into neural network functioning and could help detect unknown phase transitions in materials and quantum systems.
Researchers developed a machine learning model to quickly recognize predictive risk factors and their importance for undesirable hospitalization outcomes. The model achieved an accuracy of 95.6% and identified modifiable risk factors that can be mitigated through clinical interventions.
Neuronal silencing periods enable efficient temporal sequence identification, allowing the brain to remember phone numbers and PINs. A new AI mechanism utilizing this mechanism also protects against stolen cards by recognizing personal handwriting style and timing.
Researchers have developed a scalable, fully coupled quantum-inspired processor that can solve optimization problems efficiently. The system uses an array calculator approach to divide calculations among multiple chips, reducing data transmission and increasing performance.
Researchers at Mayo Clinic used AI to evaluate patients' ECGs, identifying new cases of atrial fibrillation that would not have been detected through routine care. The study showed that AI can target screening to high-risk patients, potentially reducing undiagnosed strokes and deaths worldwide.
A recent grant will fund a project developing new hardware for machine learning, aiming to curb unsustainable energy use in AI systems. The new algorithms being developed are made available to the research community and compatible with an openly shared computing platform.
The MEMQuD project investigates quantum effects in memristive devices, which could lead to breakthroughs in AI, cryptography, and computation. Researchers analyze fundamentals and applications of quantized conductivity phenomena at room temperature.
Current AI models are restricted by a lack of experience in real-world environments, despite achieving significant advancements in virtual settings. Researchers are now exploring ways to bridge this gap with foundation models that can operate in physical spaces.
A public consultation is launched to develop best-practice standards for diverse and inclusive healthcare datasets used in Artificial Intelligence. The project aims to reduce biases that affect patients from minoritised racial/ethnic groups, ensuring they receive accurate predictions and treatments.
Physicists used machine learning to compress a complex quantum problem into four equations, capturing the physics of electrons on a lattice with high accuracy. The approach could revolutionize how scientists investigate systems containing many interacting electrons and potentially aid in designing materials with sought-after properties.
A new study by Eurac Research confirms negative global trends in mountain areas, with an average decline of 15 days of snow on the ground over 38 years. Snow cover has decreased by 4% globally, with peaks of 20 or more fewer days in Canada's western provinces.
Researchers developed an AI-based screening method that models drug and target protein interactions using natural language processing techniques. The technique achieved high accuracy in identifying promising drug candidates, which can accelerate the exploration of new medicines and repurpose existing ones.
A recent study found that individuals who distrust fellow humans tend to have more trust in artificial intelligence's ability to moderate content online. The researchers suggest personalizing interfaces based on individual differences can positively alter user experience.
Researchers developed an in-home wireless device that monitors a patient's movement and gait speed to track Parkinson's disease progression. The device uses machine-learning algorithms to analyze over 200,000 data points collected from 50 participants, showing that it can effectively track the severity of the disease.
Sooby's research aims to develop an AI algorithm to rapidly provide high-quality data during alloy fabrication, reducing time and cost. Her lab collaborates with Computer Science professor Amanda Fernandez to leverage deep learning approaches for image labeling, increasing efficiency by 99%.
Researchers at POSTECH have developed a method to engineer organs at scale using bioprinting, overcoming previous limitations of small tissue size and functional complexity. This innovation holds promise for personalized treatment of patients with the potential to create more realistic engineered organs.
UC San Diego researchers created the first diagnostic tool to differentiate between Kawasaki Disease and Multisystem Inflammatory Syndrome in Children (MIS-C) using a novel artificial intelligence-guided algorithm. The algorithm achieved accuracy exceeding 90% with simple test results and physical exam features.
A study from the University of Georgia shows people who rely on algorithms for creative tasks don't improve their performance and are more likely to trust low-quality advice. Participants preferred algorithm-derived advice over human-based advice, even when confident in their answers.
A University of Missouri researcher and his team are using artificial intelligence to study anatomical research, creating detailed 3D computer models of muscles. This technology is advancing the field by enabling researchers to analyze muscle fiber orientation and develop a better understanding of motor control in animals.
A large-scale experimental study by Harvard, Stanford, and MIT researchers found that weaker social connections on LinkedIn have a greater beneficial effect on job mobility than stronger ties. Weaker ties increased the likelihood of job mobility the most, while strongest ties had the least impact.