Evaluating AI-generated patient education materials for spinal surgeries: Comparative analysis of readability and DISCERN quality across ChatGPT and deepseek models [0.03%]
评估AI生成的脊柱手术患者教育材料：ChatGPT和deepseek模型的可读性和DISCERN质量比较分析
Mi Zhou,Yun Pan,Yuye Zhang et al.
Mi Zhou et al.
Background: Access to patient-centered health information is essential for informed decision-making. However, online medical resources vary in quality and often fail to accommodate differing degrees of health literacy. Th...
Impact of patients' personality traits on digital health Adoption Strategies for family practices [0.03%]
患者人格特质对数字健康采纳策略的影响的家庭实践研究
Julian Beerbaum,Sibylle Robens,Leonard Fehring et al.
Julian Beerbaum et al.
Background: Various governments highlight the relevance of digitalization in family practices; however, still some adoption barriers persist due to an inadequate understanding of why patients engage in digital use cases. ...
Online professionalism through the lens of medical students and residents: A focus group study [0.03%]
医学生和住院医生眼中的在线职业规范：集中小组研究
Sebastiaan A Pronk,Simone L Gorter,Scheltus J van Luijk et al.
Sebastiaan A Pronk et al.
Purpose: Social media influences the practice of healthcare professionals. Existing studies on online professionalism and social media are scarce, and most used survey-based methods. This qualitative study explores online...
Development and validation of an interpretable machine learning model for predicting in-hospital mortality for ischemic stroke patients in ICU [0.03%]
用于预测ICU中缺血性卒中患者住院死亡率的可解释机器学习模型的开发和验证
Xiao Luo,Binghan Li,Ronghui Zhu et al.
Xiao Luo et al.
Background: Timely and accurate outcome prediction is essential for clinical decision-making for ischemic stroke patients in the intensive care unit (ICU). However, the interpretation and translation of predictive models ...
Predictive value of machine learning for in-hospital mortality risk in acute myocardial infarction: A systematic review and meta-analysis [0.03%]
机器学习对急性心肌梗死住院死亡风险的预测价值：系统评价和荟萃分析
Yuan Zhang,Huan Liu,Qingxia Huang et al.
Yuan Zhang et al.
Background: Machine learning (ML) models have been constructed to predict the risk of in-hospital mortality in patients with myocardial infarction (MI). Due to diverse ML models and modeling variables, along with the sign...
A qualitative study exploring electronic health record optimisation activities in English hospitals [0.03%]
探索英格兰医院电子健康记录优化活动的定性研究
Kathrin Cresswell,Susan Hinder,Robin Williams
Kathrin Cresswell
Background: Hospitals increasingly implement complex electronic health record (EHR) systems to improve quality, safety and efficiency. Whilst many aspects surrounding implementation and adoption processes have been resear...
PlasmaCell CAD: A computer-aided diagnosis software tool for plasma cell recognition and characterization in microscopic images [0.03%]
等离子细胞CAD：用于显微图像中等离子细胞识别和特征化的计算机辅助诊断软件工具
Rasoul Kasbgar,Alireza Vard
Rasoul Kasbgar
Background and objective: In the traditional diagnostic process for multiple myeloma cancer, a pathologist screens prepared blood samples using a microscope to detect, classify, and count plasma cells. This manual approac...
Stress monitoring using low-cost electroencephalogram devices: A systematic literature review [0.03%]
使用低成本脑电图设备进行压力监测的系统文献回顾
Gideon Vos,Maryam Ebrahimpour,Liza van Eijk et al.
Gideon Vos et al.
Introduction: The use of low-cost, consumer-grade wearable health monitoring devices has become increasingly prevalent in mental health research, including stress studies. While cortisol response magnitude remains the gol...
Artificial intelligence based predictive tools for identifying type 2 diabetes patients at high risk of treatment Non-adherence: A systematic review [0.03%]
基于人工智能的预测工具，用于识别高风险不遵守治疗方案的二型糖尿病患者：系统性回顾
Malede Berihun Yismaw,Chernet Tafere,Bereket Bahiru Tefera et al.
Malede Berihun Yismaw et al.
Aims: Several Artificial Intelligence (AI) based predictive tools have been developed to predict non-adherence among patients with type 2 diabetes (T2D). Hence, this study aimed to describe and evaluate the methodological...
RelAI: an automated approach to judge pointwise ML prediction reliability [0.03%]
RelAI：一种自动判断ML预测可靠性的方法
Lorenzo Peracchio,Giovanna Nicora,Enea Parimbelli et al.
Lorenzo Peracchio et al.
Objectives: AI/ML advancements have been significant, yet their deployment in clinical practice faces logistical, regulatory, and trust-related challenges. To promote trust and informed use of ML predictions in real-world...