NPJ Digital Medicine最新文献_第5页

Mobile application leads to psychological improvement and correlated neuroimmune function change in subjective cognitive decline 手机应用导致主观认知能力下降的心理改善和相关神经免疫功能改变

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-14 DOI: 10.1038/s41746-025-01765-1

Merav Catalogna, Nira Saporta, Bar Nathansohn-Levi, Tal Tamir, Ariel Shahaf, Shira Molcho, Shai Erlich, Shahar Shelly, Amir Amedi

{"title":"Mobile application leads to psychological improvement and correlated neuroimmune function change in subjective cognitive decline","authors":"Merav Catalogna, Nira Saporta, Bar Nathansohn-Levi, Tal Tamir, Ariel Shahaf, Shira Molcho, Shai Erlich, Shahar Shelly, Amir Amedi","doi":"10.1038/s41746-025-01765-1","DOIUrl":"https://doi.org/10.1038/s41746-025-01765-1","url":null,"abstract":"Subjective cognitive decline (SCD) is a potential early marker of neurodegeneration, with negative affective states such as depression and anxiety significantly contributing to cognitive impairment. Digital treatments show promise, yet evidence of their use and efficacy in SCD remains limited. We studied 103 individuals aged 50-65, experiencing SCD and heightened anxiety, randomly assigned to a three-week mobile app program or waitlist control. Assessments included psychological measures, immunological analysis, and for a subgroup of the participants also resting-state functional connectivity (rsFC). The intervention significantly reduced proinflammatory mediators (TNF-α, IL-17, IL-23, MCP-1, IFN-γ, and IL-12) and improved depression, anxiety, resilience and well-being with sustained effect over a three-week follow-up. RsFC results show enhanced fronto-limbic connectivity correlated with the psychological and immunological changes, with the insula emerging as a key hub mediating these relationships. These findings highlight digital treatments as potential scalable, brain-immune targeted interventions for SCD and other medical conditions.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"63 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy in consumer wearable technologies: a living systematic analysis of data policies across leading manufacturers 消费者可穿戴技术中的隐私：对领先制造商数据政策的实时系统分析

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-14 DOI: 10.1038/s41746-025-01757-1

Cailbhe Doherty, Maximus Baldwin, Rory Lambe, Marco Altini, Brian Caulfield

{"title":"Privacy in consumer wearable technologies: a living systematic analysis of data policies across leading manufacturers","authors":"Cailbhe Doherty, Maximus Baldwin, Rory Lambe, Marco Altini, Brian Caulfield","doi":"10.1038/s41746-025-01757-1","DOIUrl":"https://doi.org/10.1038/s41746-025-01757-1","url":null,"abstract":"The widespread adoption of consumer wearable devices has enabled continuous biometric data collection at an unprecedented scale, raising important questions about data privacy, security, and user rights. In this study, we systematically evaluated the privacy policies of 17 leading wearable technology manufacturers using a novel rubric comprising 24 criteria across seven dimensions: transparency, data collection purposes, data minimization, user control and rights, third-party data sharing, data security, and breach notification. High Risk ratings were most frequent for transparency reporting (76%) and vulnerability disclosure (65%), while Low Risk ratings were common for identity policy (94%) and data access (71%). Xiaomi, Wyze, and Huawei had the highest cumulative risk scores, whereas Google, Apple, and Polar ranked lowest. Our findings highlight inconsistencies in data governance across the industry and underscore the need for stronger, sector-specific privacy standards. This living review will track ongoing policy changes and promote accountability in this rapidly evolving domain.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"9 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data 使用电子病历分类数据进行临床决策的自我监督表征学习的范围综述

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-14 DOI: 10.1038/s41746-025-01692-1

Zheng Yuanyuan, Bensahla Adel, Bjelogrlic Mina, Zaghir Jamil, Turbe Hugues, Bednarczyk Lydie, Gaudet-Blavignac Christophe, Ehrsam Julien, Marchand-Maillet Stéphane, Lovis Christian

{"title":"A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data","authors":"Zheng Yuanyuan, Bensahla Adel, Bjelogrlic Mina, Zaghir Jamil, Turbe Hugues, Bednarczyk Lydie, Gaudet-Blavignac Christophe, Ehrsam Julien, Marchand-Maillet Stéphane, Lovis Christian","doi":"10.1038/s41746-025-01692-1","DOIUrl":"https://doi.org/10.1038/s41746-025-01692-1","url":null,"abstract":"The widespread adoption of Electronic Health Records (EHRs) and deep learning, particularly through Self-Supervised Representation Learning (SSRL) for categorical data, has transformed clinical decision-making. This scoping review, following PRISMA-ScR guidelines, examines 46 studies published from January 2019 to April 2024, sourced from PubMed, MEDLINE, Embase, ACM, and Web of Science, focusing on SSRL for unlabeled categorical EHR data. The review systematically assesses research trends in building computationally and data-efficient representations for medical tasks, identifying major trends in model families: Transformer-based (43%), Autoencoder-based (28%), and Graph Neural Network-based (17%) models. The analysis highlights scenarios where healthcare institutions can leverage or develop SSRL technologies. It also addresses current limitations in assessing the impact of these technologies and identifies research opportunities to enhance their influence on clinical practice.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"68 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Clinician-informed XAI evaluation checklist with metrics (CLIX-M) for AI-powered clinical decision support systems 临床医生知情的XAI评估清单和指标（CLIX-M），用于人工智能临床决策支持系统

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-14 DOI: 10.1038/s41746-025-01764-2

Aida Brankovic, David Cook, Jessica Rahman, Alana Delaforce, Jane Li, Farah Magrabi, Federico Cabitza, Enrico Coiera, DanaKai Bradford

引用次数: 0

An evaluation framework for ambient digital scribing tools in clinical applications 临床应用中环境数字划线工具的评估框架

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-13 DOI: 10.1038/s41746-025-01622-1

Haoyuan Wang, Rui Yang, Mahmoud Alwakeel, Ankit Kayastha, Anand Chowdhury, Joshua M. Biro, Anthony D. Sorrentino, Jessica L. Handley, Sarah Hantzmon, Sophia Bessias, Nicoleta J. Economou-Zavlanos, Armando Bedoya, Monica Agrawal, Raj M. Ratwani, Eric G. Poon, Michael J. Pencina, Kathryn I. Pollak, Chuan Hong

{"title":"An evaluation framework for ambient digital scribing tools in clinical applications","authors":"Haoyuan Wang, Rui Yang, Mahmoud Alwakeel, Ankit Kayastha, Anand Chowdhury, Joshua M. Biro, Anthony D. Sorrentino, Jessica L. Handley, Sarah Hantzmon, Sophia Bessias, Nicoleta J. Economou-Zavlanos, Armando Bedoya, Monica Agrawal, Raj M. Ratwani, Eric G. Poon, Michael J. Pencina, Kathryn I. Pollak, Chuan Hong","doi":"10.1038/s41746-025-01622-1","DOIUrl":"https://doi.org/10.1038/s41746-025-01622-1","url":null,"abstract":"Ambient digital scribing (ADS) tools alleviate clinician documentation burden, reducing burnout and enhancing efficiency. As AI-driven ADS tools integrate into clinical workflows, robust governance is essential for ethical and secure deployment. This study proposes a comprehensive ADS evaluation framework incorporating human evaluation, automated metrics, simulation testing, and large language models (LLMs) as evaluators. Our framework assesses transcription, diarization, and medical note generation across criteria such as fluency, completeness, and factuality. To demonstrate its effectiveness, we developed an ADS tool and applied our framework to evaluate the tool’s performance on 40 real clinical visit recordings. Our evaluation revealed strengths, such as fluency and clarity, but also highlighted weaknesses in factual accuracy and the ability to capture new medications. These findings underscore the value of structured ADS evaluation in improving healthcare delivery while emphasizing the need for strong governance to ensure safe, ethical integration.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"44 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The need to transition from paper to electronic instructions for use for diabetes devices 糖尿病设备使用说明书需要从纸质说明书过渡到电子说明书

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-13 DOI: 10.1038/s41746-025-01720-0

Cindy N. Ho, Alessandra T. Ayers, Lutz Heinemann, David C. Klonoff

引用次数: 0

FedOcw: optimized federated learning for cross-lingual speech-based Parkinson’s disease detection FedOcw：基于跨语言语音的帕金森病检测的优化联邦学习

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-13 DOI: 10.1038/s41746-025-01763-3

Changqin Quan, Zhonglue Chen, Kang Ren, Zhiwei Luo

{"title":"FedOcw: optimized federated learning for cross-lingual speech-based Parkinson’s disease detection","authors":"Changqin Quan, Zhonglue Chen, Kang Ren, Zhiwei Luo","doi":"10.1038/s41746-025-01763-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01763-3","url":null,"abstract":"Accurate detection of Parkinson’s disease (PD) through speech analysis holds great promise for early diagnosis and improved patient management. However, developing robust machine learning models is challenging due to the decentralized nature of medical data and the substantial heterogeneity in multilingual PD speech datasets. Conventional federated learning (FL) methods struggle in these heterogeneous, non-independent and identically distributed (non-IID) environments, where differences in data distributions arise from variations in language, speech content, recording conditions, medical measurement techniques, and dataset sizes. To address these challenges, we propose FedOcw, an optimized FL framework designed to enhance cross-lingual knowledge transfer and improve convergence stability. Through extensive multilingual experiments, we demonstrate that FedOcw consistently outperforms traditional FL models by achieving superior diagnostic accuracy while ensuring adaptive and equitable weight distribution across clients. These findings highlight FedOcw as an effective FL solution for privacy-preserving, speech-based PD detection across diverse linguistic and institutional settings.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"42 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncovering ethical biases in publicly available fetal ultrasound datasets 揭露公开胎儿超声数据集的伦理偏见

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-13 DOI: 10.1038/s41746-025-01739-3

Maria Chiara Fiorentino, Sara Moccia, Mariachiara Di Cosmo, Emanuele Frontoni, Benedetta Giovanola, Simona Tiribelli

引用次数: 0

How generative AI voice agents will transform medicine 生成式人工智能语音代理将如何改变医学

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-12 DOI: 10.1038/s41746-025-01776-y

Scott J. Adams, Julián N. Acosta, Pranav Rajpurkar

引用次数: 0

Wearable health devices for pediatric ophthalmology 儿童眼科可穿戴健康设备