Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
A scoping review published May 25, 2026 examined 270 peer-reviewed studies from 2002 to 2024 on machine learning in sport. It found applications across 12 subject areas, most frequently computer science, biomechanics, and sport psychology, with common uses in action recognition, injury prediction/prevention, and athlete selection/talent identification.
Published May 8 2026, this peer-reviewed study examined how blockchain and artificial intelligence are changing accounting, auditing, financial reporting, and accounting education. Using questionnaires from Chartered Accountants and audit firm professionals, it found that blockchain's immutable transparent ledger and AI automation can improve data reliability, enable real-time auditing, and reduce fraud and operational costs.
Emergency departments (EDs) worldwide face increasing pressure to optimize triage processes amidst rising patient volumes and resource constraints. Artificial intelligence (AI) has emerged as a potential solution to enhance triage accuracy and efficiency, yet its real-world clinical impact remains inadequately characterized.
Although endoscopic training has traditionally relied on mentorship and procedural volume to assess proficiency, the major gastroenterology societies have increasingly adopted competency-based assessment tools, including the Assessment of Competency in Endoscopy (ACE) and Direct Observation of Procedural Skills (DOPS), to provide a more objective evaluation of technical and cognitive skills. Despite these advances, mentor-based assessment remains susceptible to subjectivity.
Purpose The purpose of this study was to evaluate and compare four AI software programs-ChatGPT-5, Microsoft Copilot, Google Gemini (V 2.5), and OpenEvidence-in generating comprehensive dental treatment plans for minimally destructed and severely mutilated teeth using identical clinical inputs. Material and methods Ten anonymized clinical cases, each consisting of 1 intraoral photograph and 1 corresponding periapical radiograph, were independently submitted to each AI software program using a standardized prompt.
Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust. To address this, a growing body of methods has been developed to help clinicians understand and evaluate AI predictions.
By November 2025, a review in Molecular Biomedicine synthesized multi-omics strategies integrating genomics, transcriptomics, proteomics and metabolomics, with emphasis on machine learning and deep learning for horizontal and vertical integration, including single-cell and spatial technologies.
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