Machine learning
Researchers at Vanderbilt University have made a significant breakthrough in artificial intelligence, enhancing the performance of Graph Neural Networks (GNNs) in social network classification tasks. By utilizing average controllability and a novel rank encoding method, the team has developed a new strategy for constructing expressive node features, addressing the limitation
Machine learning
A new study published in the Journal of Surgical Education has assessed the potential of artificial intelligence (AI) as a diagnostic support tool for surgical admissions in the Emergency Department. The research, conducted at Tallaght University Hospital, compared the accuracy of AI-driven diagnoses with those made by on-call surgical trainees.
Machine learning
Researchers at Peking University Third Hospital have developed a machine learning diagnostic system that integrates Scheimpflug tomography, corneal biomechanics, and clinical risk factors to improve early detection of keratoconus. The prospective, multicenter, cross-sectional study involved 1,035 eyes from 1,035 participants across five centers between 2020 and 2024. The
Machine learning
Research from the Islamic Azad University in Tabriz, Iran, has utilized machine learning algorithms to identify predictive factors for non-alcoholic fatty liver disease (NAFLD) in children. The study drew on data from 659 children who underwent liver biopsy at Mofid Children's Hospital between 2011 and 2023. The findings
Machine learning
Research at the Medical University of Graz has explored the current state of artificial intelligence (AI) and machine learning (ML) in general pediatric surgery, focusing on five key conditions. The study found that AI and ML models have rapidly transformed healthcare, improving diagnostic image interpretation, predictive modeling, and personalized treatment
Machine learning
Researchers from Sichuan University in Chengdu, People's Republic of China, have made significant advancements in using deep learning and artificial intelligence to diagnose migraine, a neuropsychiatric disorder. By combining multiple functional magnetic resonance imaging (fMRI) indicators with explainable artificial intelligence (XAI) techniques, the researchers aimed to improve the
Machine learning
Researchers from the National Center for Radiation Research and Technology have made a significant contribution to environmental monitoring with their novel machine learning approach for anomaly detection in gamma-ray spectra. This innovative technique combines neural network modeling with bio-inspired optimization, enabling the identification of anomalies even at low source to
Machine learning
Research has shown that artificial intelligence (AI) can improve the clinical and rehabilitation management of knee osteoarthritis (KOA) by empowering nurses to deliver more effective, tailored care. A systematic review conducted by researchers from Dalian University Affiliated Xinhua Hospital explored the integration of AI in KOA management, highlighting its unique
Machine learning
Researchers at Link Campus University in Italy have investigated the usefulness, quality, and readability of responses generated by three artificial intelligence large language models (AI-LLMs) - ChatGPT-4, Gemini, and Microsoft Copilot - to frequent questions about Burning Mouth Syndrome (BMS). The study aimed to evaluate the potential of AI-LLMs in
Machine learning
Researchers at the University of Montpellier have made a breakthrough in understanding species evolution and enabling functional annotation transfer by inferring protein homology from sequence information. They represented proteins with a biologically oriented large language model and applied k-means clustering to the embedded data to extract homology relationships. This approach
Machine learning
Machina Labs, a pioneer in AI-driven manufacturing, has been selected by the U.S. Air Force Research Laboratory (AFRL) to develop advanced AI and machine learning capabilities for its flagship RoboCraftsmantm platform. The multiyear contract, in partnership with the Advanced Robotics for Manufacturing (ARM) Institute, aims to automate tool path
Machine learning
Researchers from Fondazione Policlinico Universitario Agostino Gemelli IRCCS have developed a hierarchical machine learning model to predict 12-month SLE activity in patients with systemic lupus erythematosus (SLE). This innovative approach considers patient demographics, laboratory, clinical features, treatments, and pathways, resulting in a reliable tool for predicting SLE activity. The model
