Large language models (LLMs) demonstrate significant potential in healthcare applications, but clinical deployment is limited by privacy concerns and insufficient medical domain training. This study investigated whether retrieval-augmented generation (RAG) can improve locally deployable LLM for radiology contrast media consultation. In 100 synthetic iodinated contrast media consultations we compared Llama 3.2-11B (baseline and RAG) with three cloud-based models-GPT-4o mini, Gemini 2.0 Flash and...

CONCLUSIONS: Religiosity/spirituality appears to play a significant role in the maintenance and recovery of positive symptoms of psychosis. CBTp could be enhanced by integrating religiosity and spirituality into assessment, formulation, and the development of targeted interventions. This approach would promote more culturally adapted CBTp and improved engagement with clients from diverse cultural backgrounds in Early Intervention Services.

CONCLUSIONS: Our framework established a new paradigm for spectral CT reconstruction through the synthesis of the model-driven low-rank prior with a data-driven deep prior, which yielded mutual enhancement and complementarity, collectively improving the overall quality of the reconstructed images. This dual mechanism enables comprehensive utilization of measurement signals while preventing hallucinated structures-a critical advancement for clinical applications where artifact-induced...

CONCLUSIONS: The findings highlight notable differences in precision and recall between models, particularly in extracting names and age-related information. There were challenges in processing unstructured medical text, including variability in model performance across data types. Our findings demonstrate the feasibility of integrating LLMs into health care workflows; LLMs offer substantial improvements in data accessibility and support clinical decision-making processes. In addition, the paper...