Zijie Cheng,Ariel Yuhan Ong,Siegfried K Wagner et al.
Zijie Cheng et al.
Vision-language models (VLMs) show promise for answering clinically relevant questions, but their robustness to medical image artefacts remains unclear. We evaluated VLMs' robustness through their performance on images with and without weak...
Clinical validation of an AI-assisted system for real-time kidney stone detection during flexible ureteroscopic surgery [0.03%]
Chenfeng Wang,Haomin Liang,Hairui Chen et al.
Chenfeng Wang et al.
Flexible ureteroscopy (FURS) is a minimally invasive, standard treatment for kidney stones. This study presents the development and clinical validation of an artificial intelligence system during FURS (AiFURS) for real-time detection, class...
Ke Ma,Min Zheng,Wenli Chen et al.
Ke Ma et al.
Lung cancer remains the top cause of cancer death, demanding consistent decisions. This clinically oriented review synthesizes computer-aided diagnosis across classical imaging, machine learning, and deep learning, emphasizing bedside-prove...
Machine learning identifies TIME subtypes linking EGFR mutations and immune states in lung adenocarcinoma [0.03%]
机器学习在肺癌腺癌中识别时间亚型连接表皮生长因子受体突变和免疫状态
Zetian Gong,Mingjun Du,Ying Li et al.
Zetian Gong et al.
Epidermal growth factor receptor (EGFR) mutation is a key oncogenic driver in lung adenocarcinoma (LUAD), but its impact on the tumor immune microenvironment (TIME) remains unclear. By integrating single-cell transcriptomes from 153 LUAD sa...
Trends and effectiveness of digital health management in postdischarge coronary artery disease across COVID19 pandemic [0.03%]
新冠肺炎流行下数字健康管理体系在冠心病患者院后的应用趋势及其有效性分析
Zuoxiang Wang,Lanshu Yang,Sheng Zhao et al.
Zuoxiang Wang et al.
The effectiveness of digital health management (DM) in post-discharge care for patients with coronary artery disease (CAD) remains insufficiently explored, particularly across public health emergencies. A total of 24,129 patients with CAD e...
Artificial intelligence-enabled analysis of handheld single-lead electrocardiograms to predict incident atrial fibrillation: an analysis of the VITAL-AF randomized trial [0.03%]
基于人工智能的手持单导联心电图预测阵发性房颤：VITAL-AF随机对照试验结果分析
Shaan Khurshid,Sam F Friedman,Mostafa A Al-Alusi et al.
Shaan Khurshid et al.
Whether artificial intelligence (AI) analysis of single-lead ECG (1 L ECG) can predict incident AF is unknown. In the VITAL-AF trial (ClinicalTrials.gov NCT03515057, registered 2/24/2021) of primary care patients aged ≥65 years undergoing ...
A user-driven consent platform for health data sharing in digital health applications [0.03%]
一种用户驱动的健康数据共享数字平台 Datenschutzprinzipen in Cloud Computing - Technische und rechtliche Aspekte
Stefanie Brückner,Akrem Dridi,Aniruddha Deshmukh et al.
Stefanie Brückner et al.
Wearable and health app data hold significant potential for healthcare and research, yet fragmented consent mechanisms create challenges for ethical, transparent, and user-controlled data sharing. We describe a centralised consent managemen...
A systematic review of explainable artificial intelligence methods for speech-based cognitive decline detection [0.03%]
基于语音的认知衰退检测的可解释人工智能方法系统综述
Ravi Shankar,Ziyu Goh,Fiona Devi et al.
Ravi Shankar et al.
Artificial intelligence models analyzing speech show remarkable promise for identifying cognitive decline, achieving performance comparable to clinical assessments. However, their "black box" nature poses significant barriers to clinical ad...
Knockoff-ML: a knockoff machine learning framework for controlled variable selection and risk stratification in electronic health record data [0.03%]
基于电子健康记录数据的受控变量选择和风险分层的 Knockoff-ML 框架
Qi Wang,Linyan Li,Yi Yang
Qi Wang
Effective risk stratification is essential in clinical practice, enabling better resource allocation and improved patient outcomes. Although machine learning models have been widely used for risk prediction and stratification in electronic ...
Academia loses its grip on digital health solutions as innovation in artificial intelligence shifts towards industry [0.03%]
学界对数字健康解决方案的主导地位丧失，人工智能创新向业界转移
Baraa A Hijaz,Olivia M Burke,Sara Y Khattab et al.
Baraa A Hijaz et al.
Over the past decade, healthcare tools leveraging artificial intelligence and machine learning have transitioned from academic-centered development to industry. Key drivers include limited funding, rising computational requirements, and an ...