Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
In a January 2019 peer-reviewed survey, researchers described two persistent barriers for AI: data siloed as isolated islands and tightening privacy and security requirements. They proposed a comprehensive secure federated-learning framework that includes horizontal, vertical, and transfer variants, and surveyed existing work on definitions, architectures, and applications.
In work published August 20, 2021, researchers built on the CASP14-era DeepMind approach to protein folding. They developed RoseTTAFold, a three-track network that processes sequence, distance, and coordinate information simultaneously, achieving accuracies approaching those of DeepMind.
Published in May 2015, this peer-reviewed overview describes deep learning as models with multiple processing layers that learn multi-level representations of data, trained via backpropagation to adjust parameters between layers
Published in 2017, this methods paper explored Random Forest as an alternative to Cox regression for survival analysis. Using 66,807 colon cancer cases from the SEER database, the authors built both a Cox model and a Random Forest model to derive mortality-associated risk factors and compared their predictive performance.
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.
A mixed-methods study examined Midjourney, Runway ML and Stable Diffusion to understand how generative AI reshapes artistic subjectivity. Twelve practitioners were interviewed in spring 2025 to build a grounded-theory framework, followed by a survey of 426 users in summer 2025 evaluated with CRITIC weighting.
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.
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.
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