A local, privacy-oriented multi-agent LLM framework for framework-grounded manuscript editing: A proof-of-concept [0.03%]
一种面向隐私的局部多智能体LLM框架用于基于框架的稿件编辑：概念验证
Alon Gorenshtein,Rohan Bhansali,Brandon Westover et al.
Alon Gorenshtein et al.
Objectives: Manuscript preparation is a bottleneck in publishing, and cloud-based AI tools raise confidentiality concerns for clinical researchers. We developed the Paper Analysis Tool (PAT), a free, multi-agent framework...
Evaluating the efficacy of artificial intelligence in audiology: a head-to-head comparison of ChatGPT and Gemini on hearing aid management [0.03%]
评估人工 Intelligence 在听力学中的有效性：ChatGPT 和 Gemini 在助听器管理方面的直接比较
Isa Tuncay Batuk,Irem Karakuluk-Celebi
Isa Tuncay Batuk
Objective: Hearing aid users frequently require accessible and immediate assistance for daily device management. This study aims to evaluate and compare the performance of two prominent Large Language Models (LLMs)-ChatGP...
LLM-assisted clinical coding audit through an interpretable coding pipeline [0.03%]
基于可解释编码管道的LLM辅助临床编码审计
Supriya Khadka,Xiaorui Jiang,Vasile Palade
Supriya Khadka
Objective: Clinical coding is vital yet complex, often hindered by imperfect training data. This study addresses the overlooked issue of undercoding and coding errors in standard datasets and investigates their impact on ...
AI as a Clinical Co-Pilot: A Comparative Evaluation of a Locally Deployed Human-in-the-Loop Framework for Ophthalmic Surgical Record Generation [0.03%]
人工智能作为临床助手：眼科手术记录生成的本地部署人机循环框架的比较评估
Chang Zhang,Wenxuan Mao,Han Chen et al.
Chang Zhang et al.
Artificial Intelligence in the Golden Hour: A scoping review of prehospital trauma triage and implementation feasibility in LMICs [0.03%]
黄金时间里的智能创伤分诊：人工智能在中低收入国家院前创伤分类和实施可行性的系统综述
Kholisah Widiyawati,Fitrio Deviantony
Kholisah Widiyawati
Background: The Golden Hour of trauma care in Low- and Middle-Income Countries (LMICs) is routinely compromised by systemic deficits, including unmapped infrastructure, chronic traffic congestion, and a critical scarcity ...
Culturally and linguistically adapted digital diabetes health applications improve self-management and clinical outcomes in racialized immigrants: a systematic scoping review [0.03%]
文化与语言适应型数字糖尿病健康应用程序可改善种族化移民的自我管理及临床结果：系统性综述研究
Divine Budzi,Mirey Karavetian,Ammar Saad et al.
Divine Budzi et al.
Background: Despite public health efforts, the prevalence of diabetes has quadrupled since 1990, disproportionately impacting marginalized communities including immigrants and refugees. Immigrants and refugees face access...
Interpretable mortality prediction at VA-ECMO establishment in acute myocardial infarction: A multicenter study from the CSECLS registry [0.03%]
急性心肌梗死VA-ECMO建立期可解释的死亡率预测：CSECLS注册中心多中心研究
Haitao Bian,Beilei Yuan,Peng Wu et al.
Haitao Bian et al.
Background: Despite the growing use of venoarterial extracorporeal membrane oxygenation (VA-ECMO) as a rescue therapy for acute myocardial infarction (AMI), in-hospital mortality remains high. AMI-specific evidence on pre...
How large language models can be used for teamwork and communication in healthcare settings: A scoping review [0.03%]
大型语言模型在医疗环境中团队合作和沟通中的应用：一项范围回顾研究
Ilse Super,Olya Rezaeian,Onur Asan
Ilse Super
Background: Generative artificial intelligence, particularly large language models (LLMs), has rapidly advanced and shows promise in healthcare for supporting teams through their ability to understand and generate medical...
Mehakpreet Kaur,Tamanna Jannat Promi,Lovin Gopali et al.
Mehakpreet Kaur et al.
Background and objectives: The COVID-19 pandemic expedited the use of virtual hospital services (VHS) and hybrid hospital-in-the-home (HITH) models. However, the current evidence base remains fragmented and heterogeneous ...
AI-powered radiology report simplification in Arabic: A prospective evaluation of patient-perceived understandability and clinical safety [0.03%]
基于人工智能的放射学报告阿拉伯语简化：一项关于患者感知理解力和临床安全性的前瞻性评估研究
Mohammad I Alsayed,Mohammed Moeenaldeen Alsayed,Akram Maghrabi et al.
Mohammad I Alsayed et al.
Background: Radiology reports are written for clinicians, leaving the majority of patients unable to understand their own imaging findings. Large language models (LLMs) offer a means to simplify reports for patient-facing...