Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
As of November 2025, this peer-reviewed review examines agentic AI in newsrooms that can autonomously plan, decide, and generate content. Analyzing 46 sources from 2015-2025, it finds current evaluations emphasize technical accuracy and efficiency while neglecting trust, governance, and collaboration.
By August 2026, a multi-omics study combined single-cell and bulk RNA-seq from GEO with three machine learning algorithms to screen for kidney stone drivers, identifying GLS and LPIN2 as upregulated in fibroblasts and building a risk prediction nomogram.
Researchers used a Causal Forest causal machine learning model on a retrospective cohort of gastric cancer patients treated between 2007 and 2017 to estimate who benefits from adjuvant chemotherapy. The model identified lymph node ratio as the most dominant predictor of benefit, with a significant interaction at a 0.25 threshold in a propensity-matched Stage II-III cohort.
On August 22, 2026, researchers reported an integrative analysis combining single-cell RNA-sequencing with bulk transcriptomic and clinical data from TCGA-LIHC, GEO, and ICGC to find macrophage-associated biomarkers in hepatocellular carcinoma. They derived a 16-gene signature that stratified tumors by survival and clinicopathological features, with ridge regression showing AUC 0.988 in TCGA-LIHC and 0.895-0.924 externally, and XGBoost SHAP identifying LGALS1 as highly informative.
Researchers built an 18-feature model across basic information, training, fitness and lifestyle dimensions for 800 Chinese university football players from a Kaggle dataset, comparing 10 algorithms and finding SVM best at 95.6% accuracy with SHAP highlighting stress, sleep and balance as key predictors.
The peer-reviewed article examines how AI has become an intimate presence in mental health through mood-tracking apps, emotion wearables, and therapeutic chatbots like Woebot and Wysa. It argues these systems enable cognitive offloading by aggregating biometric and self-report data and delivering CBT-based prompts, while simultaneously risking cognitive overload.
By November 2025, this peer-reviewed review synthesized how artificial intelligence bridges the cancer multi-omics data deluge to clinical decisions, integrating genomics, transcriptomics, proteomics, metabolomics and radiomics using deep learning, graph neural networks, transformers, and explainable AI.
Published June 12, 2026, this peer-reviewed analysis examines generative AI's shift of music production from specialist studios to prompt-based, platform-mediated creation, focusing on folk, rock, and protest traditions. Using a qualitative critical-hermeneutic approach, it analyzes selected songs and literature on text-to-music generation, copyright, and embodied creativity.
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