Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Published August 29, 2026, this peer-reviewed synthesis reviews 2020-2025 literature on machine learning for mapping potentially toxic elements in soils. It finds growing use of ML with environmental covariates but persistent use of spatially naive validation, limited interpretation, and incomplete uncertainty reporting.
By August 2026, a review in Clinical Microbiology and Infection summarized evidence on using unsupervised machine learning to derive clinical subphenotypes of bloodstream infections. The strongest data were in Staphylococcus aureus bacteraemia, where latent class and cluster analyses identified distinct subgroups with different mortality across cohorts, with emerging work in ICU mixed-pathogen and transplant populations and early bedside calculators for phenotype assignment.
Researchers built an integrated risk index-machine learning framework based on source-pathway-receptor concepts to evaluate potentially toxic elements in farmland soils of a mining city, combining improved Nemerow index, potential ecological risk index, Monte Carlo health risk assessment, and XGBoost regression with SHAP interpretation.
Researchers applied a machine learning model called Mosamatic to routine CT staging scans from 615 patients with stage II-III colon cancer treated between 2015-2021 at one center to quantify skeletal muscle area, density, and fat. They found myosteatosis was more common in those who later recurred and was linked to worse recurrence-free survival at 1 and 5 years.
In a December 2024 randomized lab experiment, 117 university students completed a writing task with support from ChatGPT, a human expert, analytics tools, or no extra support. Researchers measured intrinsic motivation, self-regulated learning processes, and performance, finding no motivation differences but different process patterns and higher essay score gains for the ChatGPT group without higher knowledge gain or transfer.
Published January 9 2025 in the British Journal of Biomedical Science, this peer-reviewed review examines how generative AI is being integrated into higher education, noting opportunities for personalised learning and innovative assessment alongside challenges to integrity and equity.
A mixed-method study of 666 participants examined how AI tool usage relates to critical thinking, with cognitive offloading as a mediating factor. By publication on Jan 3 2025, authors reported a significant negative correlation between frequent AI use and critical thinking abilities.
This peer-reviewed review from January 2025 examined how AI technologies including robotics, machine learning, deep learning, and natural language processing are being applied in healthcare. Drawing on Web of Science literature from 2014-2024 and case studies such as Google Health and IBM Watson Health, it reported growth in publications and use in patient interaction, predictive analytics, and remote monitoring.
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