Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Researchers introduced GDPval, a benchmark of real-world economically valuable tasks spanning 44 occupations and the top 9 U.S. GDP sectors, built from work of experienced industry professionals. As of January 2026, they reported frontier models improving linearly and approaching expert deliverable quality, with potential to complete tasks cheaper and faster than unaided experts when paired with human oversight.
Published March 22, 2024, this peer-reviewed survey examines the shift from agents trained with limited knowledge in isolated environments to agents built on large language models trained on vast Web knowledge. The authors propose a unified construction framework and systematically review applications and evaluation methods.
This peer-reviewed review from March 2024 surveys how artificial intelligence is being integrated across hospitals and clinics, covering clinical decision support, operational management, medical image analysis, and patient monitoring with AI-powered wearables, drawing on case studies of domain-specific transformation.
Published April 3 2024, this peer-reviewed study investigated AI implementation in manufacturing SMEs in Sweden across packaging, plastic, and metal sectors. It found SMEs build an AI resource portfolio through acquiring and accumulating resources, bundle them into learning and governance capabilities, and leverage them in production.
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.
On August 24, 2026, a commentary in the Journal of Veterinary Diagnostic Investigation argued that veterinary diagnostic laboratories should decide what AI outputs are permitted to do in service, not just how accurate models are. It defines service entry as the moment an output can influence triage, interpretation, draft reports, or result release, and proposes a standard operating procedure covering intended use, reviewer and signer roles, disclosure, input compatibility, refusal conditions, pathologist override, QC monitoring, and stop rules.
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