Navigating the landscape of medical artificial intelligence reporting guidelines [0.03%]
医学人工智能报告规范的现状与发展展望
The Lancet Digital Health
The Lancet Digital Health
How can artificial intelligence transform the training of medical students and physicians? [0.03%]
人工智能如何变革医学生和医师的培养？
Yilin Ning,Jasmine Chiat Ling Ong,Haoran Cheng et al.
Yilin Ning et al.
Advances in artificial intelligence (AI), particularly generative AI, hold promise for transforming medical education and physician training in response to increasing health-care demands and shortages in the global health-care workforce. Me...
Digital adherence technology interventions to reduce poor end-of-treatment outcomes and recurrence among adults with drug-sensitive tuberculosis in Ethiopia: a three-arm, pragmatic, cluster-randomised, controlled trial [0.03%]
数字依从性技术干预在减少埃塞俄比亚成人敏感结核病患者治疗结束时的不良结局和复发方面的效果：一项三臂务实集群随机对照试验
Amare W Tadesse,Mamush Sahile,Nicola Foster et al.
Amare W Tadesse et al.
Background: The effect of digital adherence technologies (DATs) on long-term tuberculosis treatment outcomes remains unclear. We aimed to assess the effectiveness of DATs in improving tuberculosis treatment outcomes and r...
Causal deep learning for real-time detection of cardiac surgery-associated acute kidney injury: derivation and validation in seven time-series cohorts [0.03%]
用于实时检测心脏手术相关急性肾损伤的因果深度学习：在七个时间序列队列中的派生和验证
Qin Zhong,Yuxiao Cheng,Zongren Li et al.
Qin Zhong et al.
Background: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a complex complication substantially contributing to an increased risk of mortality. Effective CSA-AKI management relies on timely diagnosis and inte...
Development and external validation of a clinical prediction model for new-onset atrial fibrillation in intensive care: a multicentre, retrospective cohort study [0.03%]
一项针对重症监护中新发房颤的临床预测模型的开发和外部验证：多中心回顾性队列研究
Jonathan P Bedford,Oliver Redfern,Stephen Gerry et al.
Jonathan P Bedford et al.
Background: New-onset atrial fibrillation, a condition associated with adverse outcomes in the short and long term, is common in patients admitted to intensive care units (ICUs). Identifying patients at high risk could in...
Computer-aided reading of chest radiographs for paediatric tuberculosis: current status and future directions [0.03%]
计算机辅助阅读儿童胸部X光片以检测结核病的现状及未来方向
Mackenzie DuPont,Robert Castro,Sandra V Kik et al.
Mackenzie DuPont et al.
Computer-aided detection (CAD) systems for automated reading of chest x-rays (CXRs) have been developed and approved for tuberculosis triage in adults but not in children. However, CXR is frequently the only adjunctive tool for clinical ass...
Characterising non-household contact patterns relevant to respiratory transmission in the USA: analysis of a cross-sectional survey [0.03%]
描述美国呼吸道传染病传播的非家庭接触模式：横断面调查分析
Juliana C Taube,Zachary Susswein,Vittoria Colizza et al.
Juliana C Taube et al.
Background: Interpersonal contact has a crucial role in the transmission of infectious diseases. Characterising heterogeneity in contact patterns across individuals, time, and space is necessary to inform accurate estimat...
How CHART (Chatbot Assessment Reporting Tool) can help to advance clinical artificial intelligence research through clearer task definition and robust validation [0.03%]
CHART（聊天机器人评估报告工具）如何通过更清晰的任务定义和稳健的验证来帮助推进临床人工智能研究
Arun James Thirunavukarasu,Ernest Lim,Bright Huo
Arun James Thirunavukarasu
Re-engineering a machine learning phenotype to adapt to the changing COVID-19 landscape: a machine learning modelling study from the N3C and RECOVER consortia [0.03%]
适应不断变化的COVID-19形势重新设计机器学习表型：N3C和RECOVER联盟的机器学习建模研究
Miles Crosskey,Tomas McIntee,Sandy Preiss et al.
Miles Crosskey et al.
Background: In 2021, we used the National COVID Cohort Collaborative (N3C) as part of the National Institutes of Health RECOVER Initiative to develop a machine learning pipeline to identify patients with a high probabilit...
Assessing genotype-phenotype correlations in colorectal cancer with deep learning: a multicentre cohort study [0.03%]
基于深度学习的结直肠癌基因型表型相关性多中心队列研究
Marco Gustav,Marko van Treeck,Nic G Reitsam et al.
Marco Gustav et al.
Background: Deep learning-based models enable the prediction of molecular biomarkers from histopathology slides of colorectal cancer stained with haematoxylin and eosin; however, few studies have assessed prediction targe...