Articles tagged with Artificial Intelligence
The EDAI framework is a comprehensive guideline designed to embed equity, diversity, and inclusion (EDI) principles throughout the artificial intelligence (AI) lifecycle. It addresses a significant gap in current AI development and implementation practices in health and oral healthcare, ensuring AI systems are socially responsible and ...
The NJIT team's Limelight Rainforest developed a drone-delivered technology capable of identifying thousands of rainforest species in mere hours. The device uses telemetry, satellite communications, and AI to interpret data from advanced microphones, cameras, and capture systems.
The In-Space Physical AI Workshop, held at Rice University's Ion District, brought together top scientists and experts to explore AI applications in space. Key findings included the potential of AI to streamline spacecraft navigation and crew health management.
A new AI program, PanEcho, has been developed to interpret echocardiography videos independently, reducing wait times and improving timely medical care. The program demonstrated accuracy in estimating continuous echocardiographic parameters and quantifying left ventricle dimensions and function.
A new study reveals that an AI algorithm can accurately detect early-stage metabolic-associated steatotic liver disease (MASLD) in patients who meet the criteria, leaving 83% undiagnosed. This highlights the need for improved screening and diagnosis methods to prevent progression to advanced liver disease.
The NASA Goddard Lidar team has developed a system that can produce higher resolution data within a smaller space, significantly increasing efficiency compared to current models. This technology, known as CASALS, uses artificial intelligence and other technologies to allow the instrument to make its own decisions while in orbit.
Researchers found that membership inference attacks on large language models (LLMs) are not effective in measuring information exposure risks. The common method used to test LLM leaks suffers from ambiguity due to the fluidity of language, making it difficult to define a representative set of non-member candidates.
A panel of bioethicists and experts emphasize that human accountability is crucial for healthcare decisions made by AI. The importance of diverse data sets was also stressed to avoid biases in AI-enabled medical technologies. Experts stress the need for collective liability among developers, programmers, and data scientists.
Neuro-oncology experts developed guidelines to standardize AI use in brain cancer diagnosis and treatment. The recommendations aim to ensure reliable clinical trial results and protect patients.
Researchers at the University of Washington have developed a headphone prototype that can create 'sound bubbles' around the wearer, allowing them to focus on conversations without external noise. The system uses AI algorithms and machine learning to track sound sources and suppress distant noises.
The partnership aims to improve the speed and accuracy of flood damage assessments by combining multi-modal data sources and a novel vision language model. The system will enable first responders to quickly allocate resources and respond more efficiently to weather disasters.
Researchers developed a new AI-powered diagnostic system, FastGlioma, which reveals invisible cancerous tissue in brain tumor surgery. The technique may delay or prevent recurrence of high-grade tumors and improve patient survival.
Researchers developed an AI-powered model called FastGlioma that can detect residual tumor tissue with high accuracy in 10 seconds. The technology outperformed conventional methods, reducing the risk of missed tumors by nearly 75%. This innovation could change the field of neurosurgery and minimize reliance on radiographic imaging.
A new study published in JAMA Network Open found that using Chat GPT Plus does not significantly improve the accuracy of doctors' diagnoses, but it outperformed conventional methods in certain cases. The researchers suggest that physicians need more training and experience with AI to capitalize on its potential.
The joint Center for Health AI will focus on making care more personalized and effective while being more efficient and manageable for physicians. AI will be used to streamline workflows and administrative tasks, reducing burnout among healthcare workers and improving patient experience.
A study by Niigata University found that AI analysis of PET/CT images can predict the occurrence of interstitial lung disease, a serious side effect of immunotherapy. Patients with high inflammation in non-cancerous lungs are at a higher risk of developing this condition.
The study emphasizes the need for a coherent approach to understanding A.I. threats, recognizing the intricate interplay between technology and society. Experts propose involving laypeople and experts in risk assessment processes, as well as promoting social resilience to ensure better decision-making.
NeuroMechFly v2 simulates how a fruit fly navigates through its environment while reacting to sights, smells, and obstacles. The model can track moving objects visually or navigate towards an odor source, while avoiding obstacles in its path, enabling researchers to study brain-body coordination and animal intelligence.
Researchers at Tufts University have discovered that AI's poor performance in conversational skills stems from its limited exposure to transcribed spoken language. The AI is trained on vast datasets of written content but lacks the nuance of human conversation, making it struggle with timing and turn-taking.
The 12th Heidelberg Laureate Forum will bring together 200 talented young researchers from around the world to interact with renowned laureates of prestigious prizes. The event offers a unique opportunity for cross-generational scientific dialogue, networking, and collaboration.
AI models trained on limited data may misdiagnose certain populations, particularly those in underserved communities. Researchers propose six key strategies to develop fair AI models, including using diverse datasets and transparent decision-making processes.
A new system combining high-speed video and an AI algorithm can detect high blood pressure and Type 1 or Type 2 diabetes from subtle changes in blood flow, potentially leading to early detection and treatment. The technology is still in its preliminary stages but holds promise for non-invasive monitoring of health at home.
The University of Washington School of Medicine has released a flagship AI-ready dataset for a type 2 diabetes study, featuring a diverse range of participants and novel measures. The dataset includes environmental sensor data, survey responses, eye-imaging scans, and traditional biologic variables.
Researchers at Mass General Brigham developed an AI algorithm to identify long COVID cases in patients' health records, revealing a higher prevalence rate than previously thought. The tool uses 'precision phenotyping' to sift through individual records and track symptoms over time, reducing bias in diagnosis.
Researchers developed a software tool called Perseus that identifies critical tasks in AI training and slows down processors not on the path, eliminating unnecessary power use. This approach saves enough energy to power 1.1 million US homes in 2026 and reduces AI's carbon footprint.
Researchers developed an innovative automated wildlife camera network to monitor species in a Dutch coastal dunes nature reserve. The system, which combines autonomous cameras, wireless data transmission and AI-driven species identification, has been shown to be cost-efficient and efficient, providing new insights into the habitat use ...
Recent advancements in diagnostic techniques, including artificial intelligence integration, biomarker discoveries, and imaging technologies, have transformed the detection of pancreatic cancer. Key findings include improved sensitivity and accuracy of early-stage lesion identification using AI models, as well as the discovery of novel...
MIT researchers developed a theoretical foundation for methods to aggregate genes into related groups, enabling the efficient learning of underlying cause-and-effect relationships. They achieve this using only observational data, potentially paving the way for targeted treatments.
Researchers trained an AI model on goat faces in pain, achieving 62-80% accuracy. This technology may help clinicians treat pain effectively in patients who cannot speak. The goal is to develop a more generalizable system for pain assessment in animals and eventually humans.
The new AI-based Natural Language to Query (NLQ) technology in Dimensions translates user intent into a structured Boolean query, ensuring consistency and transparency. This update enables users to save time while producing accurate results and previewing their search outcomes more efficiently.
A new crowdsourcing system, FireLoc, uses a network of low-cost mobile phones to detect wildfires minutes—even seconds—after they ignite. The system prioritizes privacy and accurately maps wilderness fires to within 180 feet of their origin.
Research articles highlight the limitations of the 'tens rule' in invasion ecology, while also showcasing the growing use of crowdsourced science in environmental reviews. Additionally, a study uses artificial intelligence to identify individual animals with high accuracy, improving conservation strategies.
A recent study by Ruhr-University Bochum found that 0.052% of Twitter accounts use AI-generated profile pictures, which are often linked to fake profiles spreading disinformation and propaganda. The researchers also found that these accounts had fewer followers and were more likely to be blocked by Twitter.
Researchers developed a multimodal dataset, TimelyTale, to gather passenger-specific sensor data for context-relevant explanations. The approach effectively identified the timing and frequency of passenger demands for explanations, enabling the creation of a machine-learning model to predict the best time for providing an explanation.
Hoda Eldardiry receives $349,360 grant from NSF to develop practical competencies for students to apply ethical principles in AI system design. Her team aims to engage industry professionals to translate AI ethics into concrete decision-making.
Researchers developed a new AI model, BiomedGPT, to support various biomedical tasks. The model achieved 16 state-of-the-art results in 9 biomedical tasks and demonstrated robust predictive abilities.
Researchers found that AI tools can link genetic variations with diseases like diabetes without considering the complexity of genetics. A new statistical method can reduce false positives, but proxy information studies also introduce misleading correlations, warns Qiongshi Lu.
MIT researchers develop 3D transistors using quantum mechanical properties to achieve low-voltage operation and high performance. The devices can deliver comparable performance to state-of-the-art silicon transistors while operating efficiently at much lower voltages.
The AI-powered system can detect toxic gases like nitrogen dioxide in real-time, identifying the source of harmful gas leaks. The system's optimization technique ensures fewer resources are used while providing faster and more accurate gas leak detection.
Researchers at Linköping University have developed a new version of AlphaFold that can predict the shape of very large and complex protein structures, integrating experimental data. This breakthrough aims to improve the development of new proteins for medical drugs.
Researchers at NCSA have presented a novel post-quantum cryptography network instrument to measure PQC adoption rates and ensure secure data safeguarding. The project's findings indicate that only OpenSSH and Google Chrome have successfully implemented PQC, achieving an initial adoption rate of 0.029%.
The NSF-Simons AI Institute for the Sky (SkAI), led by Northwestern University, will develop innovative AI tools capable of handling vast data from astronomical surveys. Funded by a $20 million grant, Argonne National Laboratory will help drive this revolution in astronomy.
A new study calls for the adoption of new research ethics policies to foster learning and discussion of ethical issues. The guidelines aim to shift from compliance-based ethics to promoting ethical norms and practices.
A team of computer scientists has developed an interactive AI tool that transforms static physics textbook diagrams into 3D simulations, allowing students to visualize complex concepts like momentum and electrical currents in action.
A new generative AI tool called Theseus is being developed to reduce time and labor in computational modeling, suggesting and running simulations to accelerate research and discovery. The AI assistant aims to enhance the work of computational scientists by providing scientific intuition, checking code for bugs and making suggestions.
A new reinforcement learning framework, AVATARS, uses diverse sensor streams to track whales and predict their surface locations. The framework aims to minimize missed encounters and improve data collection for Project CETI.
Researchers synthesizing AI with biomarker analysis aim to detect early signs of aging and enable targeted interventions to delay disease. This approach has the potential to slow, prevent or reverse certain effects of aging.
Researchers developed a wearable ultrasound device that tracks muscle function without invasive procedures, offering high-resolution imaging and wireless monitoring capabilities. The technology has potential applications in respiratory health and human-machine interfaces.
Two teams, led by Rice's ENRICH office, have won $100,000 in matching funds from partnering institutions to investigate AI-based symptom checkers and electrocardiogram data for improving cardiovascular risk assessment. The projects aim to mitigate vulnerabilities and reduce health disparities.
A new University of Illinois study examines the adoption of robotic weeding technology to fight superweeds, finding that forward-looking management strategies are more effective than myopic approaches. The research suggests that farmers with a long-term perspective on weed resistance will benefit from early adoption of robots.
Researchers found that Black patients are less likely to receive medical tests, leading to inaccurate AI models. A new algorithm corrects for this bias by identifying untested patients based on race and vital signs, improving model accuracy to around 60%.
Researchers developed an AI-based method to analyze kidney lesions in female patients with Alport syndrome, predicting renal prognosis and guiding treatment interventions. The approach uses a modified stain and deep learning to detect basement membrane lesions, showing a positive correlation with proteinuria concentration.
Researchers found that AI doctors who recall patients' social information, with privacy control offered, increase patient satisfaction. Human doctors don't require social or medical information to establish a close relationship with patients.
Researchers used a virtual cow game to study human movement and navigation, developing a model that can simulate human behavior and predict choices. The study found that humans make decisions based on angular distance and previous choices, and the developed model could accurately mimic these patterns.
Yihao Zheng and his team are developing a fiber-optic probe that analyzes artery blockages in the brain and guides procedures for blockage removal. The technology uses light and advanced calculations to determine the properties of blood clots, enabling doctors to make informed decisions about how to remove them.
Researchers developed an AI model that can identify and measure aggressive prostate cancer lesions with high accuracy. The model's estimates of tumor size were associated with the likelihood of cancer recurrence or metastasis.
A study by Osaka University reveals that Japanese consumers value transparency in AI assistants, compromising on performance for greater clarity. Environmental sustainability is also a consideration, but remains secondary to cost and performance.
A new study from the MIT Center for Collective Intelligence found that combining humans and AI can be effective in certain tasks, such as creative ones like summarizing social media posts or generating new content. However, human-AI combinations often underperform compared to AI alone in decision-making tasks. The researchers suggest t...
A machine learning algorithm developed by University of Cambridge researchers can detect and grade heart murmurs in dogs with high accuracy, similar to expert cardiologists. The technology has the potential to empower primary care veterinarians to provide early detection and treatment, improving quality of life for dogs.
A team of University of Houston engineers developed an AI tool to predict and control pandemic spread by analyzing international air travel. The analysis found that reducing flights in Western Europe can lead to fewer global COVID-19 cases, making it a key strategy for controlling the pandemic.