Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Researchers compared human-authored short stories with stories generated by GPT-3.5, GPT-4, and Llama 70b in response to the same prompts, using Burrows' Delta and clustering methods including hierarchical clustering and multidimensional scaling to visualize stylistic relationships.
In a quality improvement program evaluation, researchers used artificial intelligence to identify themes in interview data. By the publication date of 2026-08-24, the approach had produced four replicable themes grounded in the data, while also generating two consistently identified themes that were subtle misrepresentations.
Researchers combined MALDI-TOF mass spectrometry with eight machine learning and two deep learning models trained with 5-fold cross validation on 255 spectra from seven bacteria and five viruses, then tested three top performers against an external Robert Koch Institute database of highly pathogenic bacteria.
Published August 2026, this peer-reviewed synthesis addresses transparency as a foundational condition for trustworthy AI in healthcare. It finds current methods to operationalize transparency across AI-enabled medical devices are fragmented, and proposes a SaMD lifecycle framework to map regulatory and standards requirements to concrete development and governance steps.
As of August 2025, this peer-reviewed mixed-method review synthesized existing literature on AI in healthcare, finding that AI-driven decision support systems reshape clinical practice by offering enhanced decision-making while simultaneously being linked to deskilling and upskilling inhibition among medical professionals.
This peer-reviewed survey from September 2025 provides a comprehensive overview of Explainable Artificial Intelligence, covering foundational concepts, terminology, taxonomy of methods and application domains including healthcare, finance, law and autonomous systems.
By September 2025, a peer-reviewed review in Food Science & Nutrition described an emerging model that combines continuous glucose monitors, AI-driven meal planning, and mobile health apps to tailor nutrition for diabetes and obesity based on genetic, epigenetic, microbiome, and real-time metabolic data.
Published September 23, 2025, this peer-reviewed study systematically tested frontier Large Reasoning Models that generate detailed thinking processes before answering. Using controllable puzzle environments to vary compositional complexity, the authors analyzed final accuracy and internal reasoning traces and compared LRMs to standard LLMs under equivalent inference compute.
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