1338 published stories · page 30 of 90
Allege tech firm pirated hundreds of song lyrics and sheet music from The Beatles, Taylor Swift, Michael Jackson and other artists to train Claude models
The Globe and Mail
Hirschsprung's disease (HD) is characterised by absence of ganglion cells in the distal large intestine, requiring accurate histopathological diagnosis. Conventional diagnostic methods are time-consuming, subjective, and demand specialised expertise. While artificial intelligence (AI) shows promise for improving diagnostic capacity, its clinical utility requires rigorous evaluation. Following PRISMA 2020 guidelines, this systematic review evaluated machine and deep learning techniques for HD diagnosis from histo…
SMART technological advancements help diagnose, treat, and monitor various diseases at the earliest stages. It presents an opportunity to maintain the key components of conventional obesity management programming while reducing costs and provider time inputs. Various machine learning models have helped predict the risks of obesity and metabolic syndrome. Additionally trained convolutional neural networks can now automatically segment and quantify different adipose tissue compartments. Various multicenter series…
Cyclin-dependent kinases 4 and 6 (CDK4/6) are pivotal regulators of the G1-to-S phase transition, and their dysregulation is a hallmark of numerous malignancies. Despite the clinical success of existing CDK4/6 inhibitors, there remains a persistent need for chemically diverse scaffolds with potent dual-target affinity. In this study, we developed and implemented a virtual screening workflow that synergistically integrates ligand-based machine learning with structure-based molecular docking. By benchmarking multi…
In India, regional yield gaps continue to widen despite increased agricultural productivity, owing to socioeconomic inequality and climate variability. This study develops a novel Yield Gap Vulnerability (YGV) framework to measure agricultural fragility at the district-level by integrating agricultural, hydrological, meteorological, and socioeconomic indicators to observed yield gaps for major cereals (rice and wheat) and nutri-crops (maize and millet). An integrated Machine Learning (ML) approach is used that c…
Objectives Artificial intelligence (AI) is increasingly used in esthetic dentistry; however, its accuracy in predicting post-prosthetic facial outcomes in edentulous patients remains unclear. This study aimed to evaluate the ability of AI models to simulate post-denture facial esthetics compared with actual clinical outcomes. Materials and methods A prospective within-subject observational study was conducted on 14 completely edentulous patients receiving new complete dentures. Standardized facial photographs we…
Aims Pre-participation cardiovascular screening (PPS) is essential for preventing SCD in athletes, yet ECG interpretation requires expertise and remains resource-intensive. We aimed to evaluate the feasibility and diagnostic performance of a deep learning (DL) model for analysis of clinical data and resting 12‑lead ECG obtained during routine PPS in competitive athletes. Methods In this prospective single center observational study, competitive athletes aged 18 to 60 years and undergoing routine PPS were enrolle…
Background Previous research links frailty to cognitive decline, but the relationship between frailty and motoric cognitive risk syndrome (MCR), a dementia precursor, is underexplored. Methods This study used CHARLS data with 3388 participants aged ≥45. Data from 2011 to 2012, 2013, and 2015 were analyzed to examine the relationship between frailty index (FI), total FI, and changes in FI (△FI) with MCR risk. Nine machine learning models were built using baseline FI to predict MCR risk, and changes in frailty sta…
Predictive modelling in healthcare has advanced rapidly, yet social care systems, despite their central role in supporting vulnerable populations, remain underexplored in this domain. In this study, we apply machine learning to a large, pseudonymised dataset of social care records from 27,590 adults in Oxfordshire, encompassing around 90% of individuals receiving care in the region. We developed models to predict three outcomes of interest: future care plan needs, hospital admissions, and all-cause mortality, ev…
Background Timely vasopressor initiation is critical in fluid-refractory pediatric septic shock, yet clinicians lack objective tools to identify children requiring early hemodynamic escalation after fluid resuscitation. Methods We performed a retrospective multicenter study using electronic health record data from five pediatric emergency departments (March 2022-February 2025). Children aged 3 months-17 years screened for sepsis who received ≥2 fluid boluses and were vasopressor-naïve at the second bolus were in…
Accurate longitudinal nodule matching is a critical technical prerequisite for automated growth rate (volume doubling time) assessment in lung cancer screening. This study evaluated an artificial intelligence (AI) pulmonary nodule analysis system in all 361 UK Lung Cancer Screening (UKLS) trial participants who underwent a 3-month follow-up low-dose computed tomography (LDCT) scan. The pulmonary AI independently evaluated these baseline scans using an updated volume threshold (solid component ≥ 100 mm³ per NELSO…
Potentially toxic elements (PTEs) in soils pose persistent risks to ecosystems, groundwater, and food systems, creating a need for reliable spatial assessment tools. Machine learning (ML) is increasingly used to map PTE concentrations from environmental covariates, but many studies still rely on spatially naive validation, limited interpretation, and incomplete uncertainty reporting. This review synthesizes recent advances (2020-2025) in ML-based PTE mapping with emphasis on four requirements for monitoring-grad…
ace