Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
On August 24, 2026, a peer-reviewed study reported development and validation of an interpretable machine learning model to predict progression risk in 342 patients with normal-tension glaucoma enrolled at a tertiary hospital. The team integrated corneal biomechanical parameters, genetic risk scores, and tear molecular biomarkers, selecting predictors with LASSO and multivariable logistic regression, and compared random forest, SVM, and logistic regression models using AUC, calibration, and decision curve analysis with SHAP interpretation.
This umbrella review synthesized 27 systematic reviews with over 14 million participants to examine AI applications in mental health care between 2021 and 2025. It found AI improved early detection and risk stratification for depression, anxiety, stress, PTSD and suicidal ideation, and that chatbots and mobile platforms expanded access and engagement.
A December 2023 peer-reviewed analysis in Environmental Technology & Innovation used bibliometric methods to examine how new technologies, especially blockchain and artificial intelligence, are being applied to circular economy goals. The authors describe production and consumption as environmentally unsustainable and assess literature on opportunities and challenges.
Published December 6, 2023, this peer-reviewed literature review in Education Sciences examined 63 articles from 2010 onward on AI and machine learning in e-learning. It found adaptive algorithms are used to tailor learning paths to individual needs, with multiple studies reporting improved engagement, retention, and academic performance.
A September 2025 peer-reviewed review in Clinics and Practice synthesized 150 studies of AI in clinical medicine after screening 2047 PubMed records. It found strong diagnostic imaging performance with expert-level cancer detection, promise for CDSS in predicting sepsis and atrial fibrillation, and advances in surgical guidance, pathology diagnosis, and drug discovery via protein structure prediction.
In a peer-reviewed survey published October 15, 2025, researchers analyzed 10 recent Zero-Trust Architecture surveys and 136 primary studies from 2022-2024 and found most controls lacked real-world validation. They argue generative AI attacks exploit those gaps and propose a seven-stage Cyber Fraud Kill Chain that maps synthetic identities, context manipulation, and adversarial telemetry to NIST SP 800-207 components.
In a peer-reviewed study published October 2025, researchers conducted 39 interviews with ChatGPT, Gemini and Replika, prompting each to impersonate people in six occupational groups ranging from highly skilled professionals and humanities professors to blue-collar workers, construction workers, computer scientists and hairdressers. The qualitative analysis identified regularities in how the chatbots described everyday tastes and lifestyles that aligned with class distinctions.
Published October 17 2025, this peer-reviewed review summarizes how machine learning techniques are being used to predict mechanical behavior of composite materials from experimental and simulation data.
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