Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
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.
Published 2026-08-04, this peer-reviewed comparative case study examines passages from George Orwell's Nineteen Eighty-Four and Ross Goodwin's 2018 sensor-driven LSTM book 1 the Road to assess how generative systems handle literary coherence and creative agency.
In a study of 6509 children and adolescents aged 3-12 with prior respiratory tract infections in Beijing and Tangshan, researchers developed and compared 12 machine learning models to predict obesity risk, with LightGBM achieving the best reported performance and a deep learning sequence network used to verify the selected features.
A bibliometric review of 722 Scopus-indexed papers from 2011 to 2024 tracked how artificial intelligence has been applied to ADHD prediction. Using Python and VOSviewer, the authors found exponential growth peaking in 2023, a concentration of output in the United States and China, and a technological shift from support vector machines to deep learning with EEG becoming the favored data modality.
Published November 10 2025 in Nature Sustainability, the study models the sustainability implications of rapidly expanding generative AI server installations across the United States, projecting annual water and carbon footprints through 2030 and testing mitigation options.
This November 2025 scoping review in Trauma, Violence, & Abuse synthesized literature on deepfakes in gender-based violence. The authors screened thousands of records from mid-2024 and analyzed 64 psychology and social science articles to map how deepfakes are understood as a form of online violence against women.
A December 2025 peer-reviewed article in Patterns examines how to estimate the carbon and water footprints of data centers and AI. It finds that lack of workload-specific reporting forces researchers to approximate AI impacts from general data center metrics, and that company disclosures often do not allow even total performance to be determined.
Published December 15 2025, this peer-reviewed discourse study examined how AI for later life is framed by industry. The authors analyzed the websites of 33 AI companies selling robots to chatbots into care (nursing) homes, using concepts of breakdown and repair, delegation, and user representations to identify dominant narratives.
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