Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
A peer-reviewed study of 19 G20 countries from 2005 to 2023 used Generalized Method of Moments models to estimate how AI-related innovation relates to economic growth. The linear specification found a positive and significant effect, while the quadratic specification found a negative quadratic term indicating a concave pattern.
Researchers surveyed 853 university students from Melilla, Ceuta and Granada in 2026 with a validated questionnaire to assess whether AI is perceived as valid and reliable for mathematics teaching, analyzing responses with factor analysis and multivariate analysis of variance by gender, age and socioeconomic status.
As of September 30, 2024, researchers presented a framework called artificial intelligence-enabled intelligent assistant (AIIA) for higher education, detailing its architecture, NLP techniques, and integration with learning management systems to provide interactive, personalized support.
From October 5 to 11, 2025, researchers tested five large language models on 20 pulmonary tuberculosis questions spanning five themes, generating 100 responses and rating them with C-PEMAT-P, GQS, and seven readability measures. GPT-5 ranked highest on C-PEMAT-P followed by Doubao, GQS was similar across models, and models differed significantly on several readability indices.
Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications. This study aimed to describe the feasibility and perceived usefulness of a single-centre, expert-corrected LLM workflow for preparing preoperative anesthesia assessment drafts for complex consultation cases.
Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIUs), leading to poor patient outcomes, unnecessary treatments and significant healthcare costs. Artificial intelligence (AI)-assisted fracture detection tools are now available for use in radiology workflows; however, the impact of these technologies on patient outcomes, experiences and overall care pathways in the real-world clinical setting is limited.
To evaluate the impact of four commercially available AI solutions for chest radiography on diagnostic performance, workflow efficiency, and clinical decision-making in a real-world setting. In this prospective, monocentric, crossover reader study, five readers (one to six years of experience) assessed 1200 consecutive patients undergoing chest radiography (1861 total radiographs) for pulmonary infiltrates, pleural effusions, mediastinal masses, pneumothorax, and pulmonary nodules under five conditions: without AI and with each of four AI algorithms.
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.
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