Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
As of December 2025, researchers synthesized AI methods for enzyme engineering, using structure-prediction, generative, and reinforcement learning models combined with high-throughput screening to design and optimize enzymes, including synthetic synzymes for non-natural reactions.
In a peer-reviewed study published 2024-05-26, researchers examined generative AI in manufacturing to actualize Industry 5.0 sustainability goals. Using case studies, interviews and interpretive structural modeling, they developed a strategic roadmap identifying ten distinct functions through which generative AI can support responsible manufacturing, from data-driven production insights to resilience of operations.
Published December 5, 2025, this peer-reviewed study investigated how AI and Generative AI affect music streaming. Using two focus groups with users and with artists/performers, it explored perceptions of AI-generated music for listening and for artists' position and opportunities within the streaming model.
On 2024-06-26 this peer-reviewed position paper proposed a research agenda for human-centered AI in Industry 5.0 and the circular economy, selecting additive manufacturing as the central platform to integrate technical, social and sustainability considerations.
A November 2023 peer-reviewed study surveyed Generation Z students and Generation X and Generation Y teachers about generative AI in higher education. Gen Z respondents were generally optimistic about benefits such as productivity and personalized learning and said they intended to use the tools for educational purposes, while Gen X and Gen Y teachers acknowledged benefits but reported stronger concerns about overreliance and ethical and pedagogical implications.
Background Injuries are responsible for 950,000 deaths per year among children and adolescents under 18 years old. Trauma prediction scores are useful in determining severity and prognosis of injury patients.
Many problems in biomedicine can be posed as binary classification. When they are addressed using artificial intelligence methods, though, average performance alone does not show whether a dataset is artificial intelligence ready, whether the endpoint is clinically valid, or whether errors are unevenly distributed across patient subgroups.
Self-driving cars increasingly rely on deep neural networks to achieve human-like driving 1-3 . The opacity of these black-box planners makes it challenging to accurately anticipate when they will fail 4-6 , with potentially catastrophic consequences 7-9 .
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