TraceHealthTRV-2026-1008
Prior artificial intelligence (AI) studies have been limited to binary classification frameworks without Gartland subtype differentiation, and no diagnostic test accuracy meta-analysis specific to supracondylar fractures exists. This study
TracePolicyTRV-2026-0730
Future is for Everyone,” Zuckerberg addressed a range of topics related to AI that included datacenters, govern… Meta's release of free-to-download open-weight models framed as personalized
TraceBusinessTRV-2026-0344
The risks posed by artificial intelligence (AI) concern academics, auditors, policymakers, AI companies, and the
TraceHealthTRV-2026-0817
systematically searched PubMed/MEDLINE and Embase (January 2000-October 2025) for studies developing or validating AI… Meta-analysis of 19 studies with 100,790 participants found AI/ML models achieved pooled discrimination
TraceHealthTRV-2026-0564
fundus images achieved a summary sensitivity of 85.0% and AUC of 0.870 in meta-analysis. AI-driven diabetic retinopathy screening using ultra-widefield fundus images showed limited specificity
TraceHealthTRV-2026-0640
pooled specificity and AUC than clinicians in a 110-study meta-analysis. In prostate cancer and MRI subgroups, AI models showed lower sensitivity than clinicians, indicating inconsistent advantage across tasks
TraceHealthTRV-2026-0729
followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidance. PubMed/MEDLINE, Web of S… Systematic review found AI and machine learning may improve mortality prediction after road
TraceHealthTRV-2026-0379
BACKGROUND: The integration of artificial intelligence in healthcare has transformed clinical practice and research methodologies
TraceHealthTRV-2026-0787
Accurately predicting operative difficulty in laparoscopic cholecystectomy (LC) is foundational to personalized surgical planning and
TraceHealthTRV-2026-0321
Background: Osteogenesis imperfecta (OI) is a rare genetic disorder characterized by bone fragility and recurrent
TraceHealthTRV-2026-0638
To systematically evaluate the performance, methodological quality, and translational barriers of deep learning (DL) models