Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
By January 30, 2024, a peer-reviewed review in Sustainability synthesized 159 studies using PRISMA to assess generative AI use across seven professional sectors. It reported that tools like ChatGPT chatbots were dominant and linked to gains in institutional performance and work productivity.
Researchers analyzed Reddit posts and comments about mental health conversations with ChatGPT to understand how large language models are being used for support outside clinical settings. By October 2025, they found users described ChatGPT as accessible and non-judgmental, providing emotional support, validation, and practical help like navigating difficult conversations and preparing for therapy.
On 2026-08-06, a peer-reviewed study described an integrated assessment for a mountainous area in northern Iran covering flood, avalanche, rockfall and landslide. Authors trained ANN, RF and SVM models on 21 environmental variables and validated them against field inventories, then combined outputs with fuzzy AND, OR and GAMMA operators to distinguish compound from cumulative hazard zones.
A single-arm meta-analysis published August 6, 2026 pooled 30 independent test datasets totaling 67,266 non-contrast CT scans to compare convolutional neural networks, U-Net, and hybrid deep learning models for subdural hematoma detection. U-Net models demonstrated significantly higher sensitivity and precision, while all architectures showed consistently high specificity, diagnostic odds ratio, and accuracy.
Published June 2, 2024, this peer-reviewed study explored why university students adopt ChatGPT, examining how self-learning capabilities affect knowledge acquisition and application, how personalization relates to novelty value and benefits, and how individual impact, innovativeness, and barriers shape behavioral intention and actual use.
As of January 2026, this peer-reviewed review surveys Large Language Models applied to mathematics in both natural-style language and formal symbolic syntax suitable for automatic verification. It notes coding has emerged as a successful application of structured reasoning, while formalized mathematics has proven significantly more challenging.
Researchers trained an AI system on 9207 prostate MRI examinations from the Netherlands and tested it on 1000 examinations from the Netherlands and Norway, with a 400-case subset read by 62 radiologists from 20 countries. By June 2024 publication, the AI achieved AUROC 0.91 versus 0.86 for radiologists using PI-RADS 2.1, and at matched operating points detected 6.8% more clinically significant cancers at same specificity or 50.4% fewer false positives at same sensitivity.
Published June 4, 2024, this peer-reviewed paper examined AI in education through a Delphi study of 33 international professionals plus follow-up face-to-face discussions with international researchers. It found that effective use depends on keeping humans in the loop rather than blindly replacing human involvement.
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