Changes in emergency and primary care use after adding virtual physicians to HealthLink BC's 8-1-1 program [0.03%]
在HealthLink BC的8-1-1计划中加入虚拟医生后急诊和初级保健利用的变化趋势分析
S Cressman,K Stewart,F X Scheuermeyer et al.
S Cressman et al.
Health human resource constraints in Canada have left millions of patients without timely access to primary care (PC), leading many to attend emergency departments (ED) to see a doctor. We evaluated changes in service use and costs resultin...
SPEED-TR: a self-distilled and pre-trained transformer model for enhanced ECG detection of tricuspid regurgitation [0.03%]
基于自蒸馏和预训练的变压器模型Speed-TR用于三尖瓣反流心电图检测增强
Xiaolin Diao,Wei Xu,Huaibing Cheng et al.
Xiaolin Diao et al.
Tricuspid regurgitation (TR) remains underdiagnosed due to the lack of effective screening tools. We developed a self-distilled and pre-trained transformer model for detecting TR (SPEED-TR) from electrocardiography. The model was trained us...
Equipping mathematical models for hospital dynamics using information theory [0.03%]
基于信息论的医院动态数学模型构建方法研究
Jeremy A Balch,Jackson G Brandberg,Robert T Andris et al.
Jeremy A Balch et al.
We propose metrics from information theory to characterize the clinical and operational dynamics of hospitals. Ergodicity, the equality of time- and space-averages within a dynamical system, ensures its stationarity. Surprisal, the log-inve...
Reply to "When do large language models cross the line: "reasoning" red teaming in healthcare" [0.03%]
关于“大型语言模型在医疗保健领域的红线在哪里：“推理”红队演习”的回复
Roxana Daneshjou
Roxana Daneshjou
We appreciate Sorin et al. for highlighting critical considerations for future red teaming of large language models (LLMs) in healthcare. We agree that analyzing only final answers overlooks failures in internal reasoning and that reasoning...
Early detection of G2SCH through machine learning analysis of physical examination metrics [0.03%]
基于体检指标的机器学习分析实现冠心病的早期筛查与诊断研究
Xiying Huang,Ruizi Lin,Yixin Xiao et al.
Xiying Huang et al.
Grade 2 subclinical hypothyroidism (G2SCH) is associated with an increased risk of various diseases but is rarely detected before symptom onset. This study aims to develop a machine learning-based prediction model for G2SCH using routine ph...
Reasoning red teaming in healthcare not all paths to a desired outcome are desirable [0.03%]
医疗保健中的推理红队行动：并非所有通往预期结果的路径都是可取的
Vera Sorin,Panagiotis Korfiatis,Girish N Nadkarni et al.
Vera Sorin et al.
Chang et al. showed that large language models can produce unsafe or biased outputs even when superficially accurate. We highlight that LLMs can hide harmful reasoning if only final responses are red-teamed. Monitoring intermediate inferenc...
Automated detection of radiolucent foreign body aspiration on chest CT using deep learning [0.03%]
基于深度学习的胸部CT上透射性异物吸入的自动检测方法研究
Xiaofan Liu,Zhe Chen,Zhiyong Tang et al.
Xiaofan Liu et al.
Radiolucent foreign body aspiration (FBA) remains diagnostically challenging due to its subtle imaging signatures on chest CT scans, often leading to delayed or missed diagnoses. We present a deep learning model integrating MedpSeg, a high-...
A generalizable 3D framework and model for self-supervised learning in medical imaging [0.03%]
一种通用的3D框架和自监督学习医学图像模型
Tony Xu,Sepehr Hosseini,Chris Anderson et al.
Tony Xu et al.
Current self-supervised learning (SSL) methods for 3D medical imaging rely on simple pretext formulations and organ- or modality-specific datasets, limiting their generalizability and scalability. We present 3DINO, a cutting-edge SSL method...
Author Correction: Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson's disease [0.03%]
作者更正：整合数字步态数据与代谢组学和临床数据以预测帕金森病的预后
Cyril Brzenczek,Quentin Klopfenstein,Tom Hähnel et al.
Cyril Brzenczek et al.
Published Erratum
NPJ digital medicine. 2025 Nov 7;8(1):645. DOI:10.1038/s41746-025-02098-9 2025
Reporting guidelines for studies involving generative artificial intelligence applications: what do I use, and when? [0.03%]
关于涉及生成式人工智能应用的研究报告指南：我该如何使用，何时使用？
Bright Huo,Gary S Collins,Giovanni E Cacciamani et al.
Bright Huo et al.
With a growing number of studies applying generative artificial intelligence (GAI) models for health purposes, reporting standards are being developed to guide authors in this space. We describe the currently available reporting guidelines ...