Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
In this study, we present an innovative approach to the sustainable manufacturing of industrial parts using an explainable artificial intelligence (XAI)- based cyber-physical collaboration system for Industry 5.0. Current cyber-physical human systems (CPHSs) have been found to integrate AI only to a limited extent and often lack explainability.
Researchers surveyed 752 orthopedic surgery patients across 33 hospitals in Guangdong, China, asking them to rate standardized descriptions of AI functions such as chatbots, vision-based monitoring, and wearables for education, motion correction, and risk alerts during the hospital-to-home transition. By August 2026 publication, 80.3% reported willingness to use such systems, with care priorities moving from information and instructions in hospital to functional safety and rehabilitation support at home.
By November 2023, researchers surveyed 503 Polish state university students to test an extended UTAUT2 model of ChatGPT acceptance. Using PLS-SEM, they found habit, performance expectancy, and hedonic motivation predicted behavioral intention, while behavioral intention, habit, and facilitating conditions predicted actual use behavior.
Published December 7, 2023, this exploratory synthesis examines ChatGPT after its November 30, 2022 public release and rapid adoption, reviewing recent literature on how the tool is being used in education. It identifies potential benefits for personalized and interactive learning and for formative assessment, while also noting drawbacks.
By August 2026, a systematic review and meta-analysis of 34 studies evaluated AI and machine learning models to predict tuberculosis treatment failure. Nineteen studies with 100,790 participants were pooled, yielding an AUC of 0.836 with high heterogeneity, with tree-based and multimodal approaches common and most publications appearing after 2019.
On 2026-08-13, a peer-reviewed article reported a European-wide survey to set regulatory science research priorities for AI use in the medicine lifecycle. Authors developed 28 research questions across seven domains and collected 273 responses from regulators, industry, patients and consumers, academics, and healthcare professionals, finding convergence in rankings across groups.
A systematic review published 7 August 2026 examined 18 retrospective studies from 2014-2025 that used AI or machine learning to predict death after road traffic crashes, drawing mostly on national or regional databases, hospital records, and police or insurance tabular data.
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