Answering real-world clinical questions using large language model, retrieval-augmented generation, and agentic systems [0.03%]
利用大型语言模型、检索增强生成和代理系统回答现实世界中的临床问题
Yen Sia Low,Michael L Jackson,Rebecca J Hyde et al.
Yen Sia Low et al.
Objective: The practice of evidence-based medicine can be challenging when relevant data are lacking or difficult to contextualize for a specific patient. Large language models (LLMs) could potentially address both challe...
Automating and Evaluating Large Language Models for Accurate Text Summarization Under Zero-Shot Conditions [0.03%]
在零样本条件下自动化和评估大规模语言模型以实现准确的文本摘要
Maria Priebe Mendes Rocha,Hilda B Klasky
Maria Priebe Mendes Rocha
This study evaluates LLMs' effectiveness in generating accurate summaries under ZSL conditions and explores using retrieval augmented generation (RAG) and prompt engineering to enhance factual accuracy and understanding.
Generative AI Is Not Ready for Clinical Use in Patient Education for Lower Back Pain Patients, Even With Retrieval-Augmented Generation [0.03%]
生成式人工智能尚未准备好用于下腰痛患者的临床教育，即使采用了检索增强生成技术也是如此
Yi-Fei Zhao,Allyn Bove,David Thompson et al.
Yi-Fei Zhao et al.
Low back pain (LBP) is a leading cause of disability globally. Following the onset of LBP and subsequent treatment, adequate patient education is crucial for improving functionality and long-term outcomes. Despite advancements in patient ed...
Rag2Mol: structure-based drug design based on retrieval augmented generation [0.03%]
基于检索增强生成的结构基于药物设计(Rag2Mol)
Peidong Zhang,Xingang Peng,Rong Han et al.
Peidong Zhang et al.
Artificial intelligence (AI) has brought tremendous progress to drug discovery, yet identifying hit and lead compounds with optimal physicochemical and pharmacological properties remains a significant challenge. Structure-based drug design ...
Enhancing Pulmonary Disease Prediction Using Large Language Models With Feature Summarization and Hybrid Retrieval-Augmented Generation: Multicenter Methodological Study Based on Radiology Report [0.03%]
基于放射学报告的多中心方法论研究：使用特征摘要和混合检索增强型生成的大语言模型改进肺部疾病预测
Ronghao Li,Shuai Mao,Congmin Zhu et al.
Ronghao Li et al.
Background: The rapid advancements in natural language processing, particularly the development of large language models (LLMs), have opened new avenues for managing complex clinical text data. However, the inherent compl...
Multicenter Study
Journal of medical Internet research. 2025 Jun 11:27:e72638. DOI:10.2196/72638 2025
Retrieval augmented generation for large language models in healthcare: A systematic review [0.03%]
基于检索的增强生成在医疗保健领域大型语言模型中的应用：系统综述
Lameck Mbangula Amugongo,Pietro Mascheroni,Steven Brooks et al.
Lameck Mbangula Amugongo et al.
To address these limitations, retrieval augmented generation (RAG) grounds the responses of LLMs by exposing them to external knowledge sources.
Retrieval Augmented Generation-Enabled Large Language Model for Risk Stratification of Cutaneous Squamous Cell Carcinoma [0.03%]
基于检索增强生成的大型语言模型在皮肤鳞状细胞癌风险分层中的应用
Neil K Jairath,Vartan Pahalyants,Shayan Cheraghlou et al.
Neil K Jairath et al.
Objective: To determine whether a customized generative pretrained transformer model, trained on a comprehensive dataset with more than 1 trillion parameters and equipped with relevant focused context and retrieval augmented generation (RAG), could excel in aggregating and interpreting
Artificial Intelligence in Anesthesia Enters the Evidence-Based Era With Retrieval-Augmented Generation [0.03%]
基于证据的增强检索型生成人工智能在麻醉学中的应用新时代来临
Victor Fogagnoli A Almeida,Manoela Dantas,Andra E Duncan
Victor Fogagnoli A Almeida
FoodSky: A food-oriented large language model that can pass the chef and dietetic examinations [0.03%]
FoodSky：一种可以通过厨师和营养师考试的食物导向型大型语言模型
Pengfei Zhou,Weiqing Min,Chaoran Fu et al.
Pengfei Zhou et al.
We also developed the topic-based selective state space model and hierarchical topic retrieval augmented generation algorithms to improve FoodSky's ability to capture fine-grained food semantics and generate context-aware food-relevant text.
A Knowledge-Enhanced Platform (MetaSepsisKnowHub) for Retrieval Augmented Generation-Based Sepsis Heterogeneity and Personalized Management: Development Study [0.03%]
一种知识增强平台（MetaSepsisKnowHub）用于检索增强生成型异质性感染和个性化管理：开发研究
Chi Zhang,Hao Yang,Xingyun Liu et al.
Chi Zhang et al.
Objective: We aimed to extract reported sepsis biomarkers to provide users with comprehensive biomedical information and integrate retrieval augmented generation (RAG) and prompt engineering to enhance the accuracy, stability, and interpretability of clinical decisions recommended
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