Results 261 to 270 of about 296,058 (324)
ABSTRACT Multi‐purpose large language models (LLMs), a subset of generative artificial intelligence (AI), have recently made significant progress. While expectations for LLMs to assist systems engineering (SE) tasks are paramount; the interdisciplinary and complex nature of systems, along with the need to synthesize deep‐domain knowledge and ...
Taylan G. Topcu+3 more
wiley +1 more source
Retrieval-augmented generation improves precision and trust of a GPT-4 model for emergency radiology diagnosis and classification: a proof-of-concept study. [PDF]
Fink A+9 more
europepmc +1 more source
ABSTRACT Given the growing concern over the environmental impacts of industrial effluents, particularly from tanneries, assessing the ecotoxicological risks associated with these effluents, even after remediation treatments, is crucial. Therefore, we aimed to evaluate the potential effects of exposure to raw and treated tannery effluents with ...
Alex Rodrigues Gomes+9 more
wiley +1 more source
Abstract Background Disorders of the vestibular system are frequent in cats. This study aimed to describe the clinical presentation, diagnostic findings, underlying aetiologies and outcome of cats with peripheral vestibular syndrome (PVS). Methods This was a retrospective study of cats presented with PVS at four referral hospitals.
Jordina Caldero Carrete+5 more
wiley +1 more source
Large Language Models as Decision-Making Tools in Oncology: Comparing Artificial Intelligence Suggestions and Expert Recommendations. [PDF]
Ah-Thiane L+11 more
europepmc +1 more source
1. The emergence of generative artificial intelligence (Gen‐AI) requires rigorous validation to assess its diagnostic reliability and limitations. 2. Three Gen‐AI models (GPT‐4‐turbo, Gemini‐pro‐vision, and Claude‐3‐opus) performed inconsistently across different diagnostic environments, demonstrating significant internal variability and overall ...
Lihaoyun Huang+17 more
wiley +1 more source
A Comprehensive Analysis of a Social Intelligence Dataset and Response Tendencies Between Large Language Models (LLMs) and Humans. [PDF]
Mori E, Qiu Y, Kataoka H, Aoki Y.
europepmc +1 more source