NPJ Digital Medicine最新文献_第5页

Exploring PET and MRI brain networks across healthy controls, mild cognitive impairment, and Alzheimer’s disease 在健康对照、轻度认知障碍和阿尔茨海默病中探索PET和MRI脑网络

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-13 DOI: 10.1038/s41746-026-03354-2

Bingjie Wang,Sheng Bi,Xuetong Tao,Feng Wang,Haiyan Wang,Runxiang Huang,Xiaoyin Xu,Hairong Zheng,Dong Liang,Greta S. P. Mok,Shaozhen Yan,Zhanli Hu

引用次数: 0

Realizing the value of the divergence event in clinical human-AI interaction. 实现分歧事件在临床人机交互中的价值。

IF 18 1区医学

NPJ Digital Medicine Pub Date : 2026-10-13 DOI: 10.1038/s41746-026-03394-8

Florence X Doo, Gabriel A Brat, Sarah C Rossetti, Robert Klitzman, Charles E Binkley

引用次数: 0

Multidimensional continuous glucose monitoring metrics enable digital risk stratification across the diabetes spectrum 多维连续血糖监测指标可实现糖尿病谱系的数字化风险分层

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-13 DOI: 10.1038/s41746-026-03368-w

Jiali Lu,Yudan Ding,Bin Zhao,Fang Fang,Ben Lu,Jiaqi Huang

{"title":"Multidimensional continuous glucose monitoring metrics enable digital risk stratification across the diabetes spectrum","authors":"Jiali Lu,Yudan Ding,Bin Zhao,Fang Fang,Ben Lu,Jiaqi Huang","doi":"10.1038/s41746-026-03368-w","DOIUrl":"https://doi.org/10.1038/s41746-026-03368-w","url":null,"abstract":"Abstract Although continuous glucose monitoring (CGM) provides high-dimensional data, the usefulness of multimodal and diurnal glycemic profiles for disease characterization and risk stratification remains unknown. Here, in the iCanNet cohort of 90,757 participants, we examined the associations of 48 CGM-derived metrics with prediabetes, T2D, and T1D, compared to normoglycemia. We further employed Gaussian Mixture Model to define glycemic phenotypes and established digital glycemic risk score (DGRS) to quantify complication risks in T2D. Participants with prediabetes, T2D, or T1D exhibited distinct CGM signatures compared to normoglycemic individuals (all P < 0.001). Integration of top five metrics significantly improved diabetes classification, and calibration analyses showed agreement between predicted and observed risks. Four glycemic phenotypes were identified in T2D, with sustained hyperglycemia associated with a 59% increased risk of acute complications, and high glycemic variability associated with 75% and 41% higher risks of acute and chronic complications, respectively. The DGRS identified residual risk among patients achieving time in range ≥70%, reclassifying 25.0% and 22.3% of these individuals to high-risk groups for acute and chronic complications, respectively. In summary, this study revealed distinct CGM-derived signatures across diabetes statuses and established a multimodal framework for identifying high-risk individuals with T2D who may be overlooked by conventional biomarkers.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"159 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208664","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 “growth mindset of mental health” intervention increases the likelihood of initiating self-help on a mental health app: a randomized controlled trial 一项“心理健康成长心态”干预增加了在心理健康应用程序上启动自助的可能性：一项随机对照试验

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-13 DOI: 10.1038/s41746-026-03322-w

Sean H. Y. Toh,E. J. Horberg,Feodora R. Kosasih,Oliver Sündermann,Paul A. O’Keefe

{"title":"A “growth mindset of mental health” intervention increases the likelihood of initiating self-help on a mental health app: a randomized controlled trial","authors":"Sean H. Y. Toh,E. J. Horberg,Feodora R. Kosasih,Oliver Sündermann,Paul A. O’Keefe","doi":"10.1038/s41746-026-03322-w","DOIUrl":"https://doi.org/10.1038/s41746-026-03322-w","url":null,"abstract":"Abstract Mental health apps hold potential to improve access to treatment, but engagement remains low. One barrier may be the belief that mental health cannot be meaningfully improved. In a randomized controlled trial ( N = 252), we examined whether a brief growth-mindset-of-mental-health intervention—portraying mental health as improvable, not fixed—can initiate mental health app engagement among people with mental health needs. Participants completed baseline well-being measures (stress, anxiety and depression, and positive mental well-being) and were randomly assigned to the growth-mindset-of-mental-health or active control condition. Over the subsequent three months, we tracked whether they re-engaged with the app overall (i.e., completed at least a second self-help feature), and with features categorized as low-, moderate-, and high-intensity. Analyses revealed that participants in the growth-mindset condition (vs. control) with poorer baseline mental well-being (i.e., higher stress, anxiety and depressive symptoms, and lower positive mental well-being) were significantly more likely to show repeat app engagement. Moreover, those with higher anxiety and depressive symptoms were more likely to show repeat engagement with moderate-intensity features, while those experiencing higher stress and lower positive mental well-being were more likely to do so with both low- and moderate-intensity features. Together, these findings demonstrate the benefits of the growth-mindset intervention for successful initiation of self-help on a mental health app. Trial Registration: The trial was registered on ClinicalTrials.gov (Identifier: NCT07071311) on July 25, 2025.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"11 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208661","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

Development of a scoring system via an interpretable end-to-end neural network for prognostic stratification of patients with advanced melanoma 通过可解释的端到端神经网络对晚期黑色素瘤患者进行预后分层的评分系统的开发

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-13 DOI: 10.1038/s41746-026-03379-7

Ruihao Huang,Hao Zhu,Yue Huang,Hanrui Zhang,Yuanfang Guan,Menglun Wang,Lixia Zhang,Yutao Gong,Hong Zhao,Shiew-Mei Huang,Issam Zineh,Yuan Li Shen,Ge Feng,Steven Lemery,Marc R. Theoret,Lola Fashoyin-Aje,Shenggang Wang,Mo Tiwari,James Zou,Sebastian Thrun,Qi Liu

{"title":"Development of a scoring system via an interpretable end-to-end neural network for prognostic stratification of patients with advanced melanoma","authors":"Ruihao Huang,Hao Zhu,Yue Huang,Hanrui Zhang,Yuanfang Guan,Menglun Wang,Lixia Zhang,Yutao Gong,Hong Zhao,Shiew-Mei Huang,Issam Zineh,Yuan Li Shen,Ge Feng,Steven Lemery,Marc R. Theoret,Lola Fashoyin-Aje,Shenggang Wang,Mo Tiwari,James Zou,Sebastian Thrun,Qi Liu","doi":"10.1038/s41746-026-03379-7","DOIUrl":"https://doi.org/10.1038/s41746-026-03379-7","url":null,"abstract":"Abstract We created an interpretable end-to-end neural network (ScoreNet) which can be used to develop an easy-to-implement scoring system for risk stratification. We applied it to data from 2711 patients with advanced melanoma across 5 trials that supported new drug approvals and developed a scoring system for overall survival (OS) and progression-free survival (PFS). The scoring system was then validated in two separate trials. This score is a weighted summation based on eight baseline features (sum of longest diameter for all target lesions, lactate dehydrogenase, systemic immune inflammation index, hemoglobin, ECOG performance status, liver metastasis, pulse rate, and PD-L1 immunohistochemistry status). Using the score, patients can be classified into low, moderate, or high-risk categories of reduced OS and PFS. Risk stratification was demonstrated for patients treated with PD-1/PD-L1 inhibitors, chemotherapy, CTLA-4 inhibitors, BRAF or MEK inhibitors, and their combinations. ScoreNet-based risk stratification can be a new tool for clinical research/practice.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"21 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208663","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

AEGIS: an operational infrastructure for post-market governance of adaptive medical AI under US and EU regulatory frameworks. AEGIS：在美国和欧盟监管框架下，用于自适应医疗人工智能上市后治理的运营基础设施。

IF 18 1区医学

NPJ Digital Medicine Pub Date : 2026-10-13 DOI: 10.1038/s41746-026-03290-1

Fardin Afdideh, Mehdi Astaraki, Fernando Seoane, Farhad Abtahi

{"title":"AEGIS: an operational infrastructure for post-market governance of adaptive medical AI under US and EU regulatory frameworks.","authors":"Fardin Afdideh, Mehdi Astaraki, Fernando Seoane, Farhad Abtahi","doi":"10.1038/s41746-026-03290-1","DOIUrl":"10.1038/s41746-026-03290-1","url":null,"abstract":"<p><p>Medical AI requires governance preserving safety during continuous improvement. FDA Predetermined Change Control Plans (PCCP) and EU post-market surveillance address iterative updates but lack operational tooling. We present AEGIS, an internal governance and post-market-monitoring infrastructure designed to support the documentation of predetermined governance pathways for PCCP and Medical Devices Regulation alignment. AEGIS comprises dataset assimilation and retraining, model monitoring, and conditional decision-making modules. It utilizes a four-category taxonomy (APPROVE, CONDITIONAL APPROVAL, CLINICAL REVIEW, REJECT) and an independent ALARM signal. Instantiating AEGIS for sepsis prediction (PhysioNet) and brain-tumor segmentation (BraTS) exercised all decision categories, successfully routing failures to the critical REJECT + ALARM state (no deployable model; released model at risk). On real cross-site data, drift detection ROC-AUC rose from 0.82 to 1.00, reliably triggering near 500-patient batches with zero false alarms. This proof-of-concept utilizes illustrative clinical models; drift scoring quantifies covariate shift only, leaving concept drift for future work.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"9 1","pages":""},"PeriodicalIF":18.0,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13662203/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149183207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-based hourly prediction of emergency department demand integrating social determinants of health 基于人工智能的急诊需求小时预测，整合健康的社会决定因素

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-12 DOI: 10.1038/s41746-026-03378-8

Qixiu Hu,Yanqing Xu,Guang Li

引用次数: 0

From authoring to editing: a competency framework for trainee use of ambient AI clinical documentation. 从创作到编辑：培训生使用环境人工智能临床文件的能力框架。

IF 18 1区医学

NPJ Digital Medicine Pub Date : 2026-10-12 DOI: 10.1038/s41746-026-03351-5

Yash Raka, Reed Van Deusen, Holly Wiberg

引用次数: 0

Technology-Enabled Hospital Discharge: Consolidated Framework of Implementation Research-Informed Qualitative Study of Stakeholder Perspectives on Digital Solutions 技术支持的医院出院：实施的统一框架——利益相关者对数字解决方案观点的定性研究

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-12 DOI: 10.1038/s41746-026-03369-9

Jennifer Sumner,Renu Chandra Segaran,Emily Hwee Hoon Chew,Hui Wen Lim,De Sheng Ong,Sin Yee Peck,Pauline Chong,Wei Ling Chua,Brigitte Woo,Leonard Lee,Phillip Phan,Alexander Wenjun Yip

引用次数: 0

An agentic AI system for multi-framework communication coding 一种多框架通信编码的人工智能代理系统

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-11 DOI: 10.1038/s41746-026-03314-w

Bohao Yang,Rui Yang,Joshua M. Biro,Haoyuan Wang,Jessica L. Handley,Brianna N. Richardson,Sophia Bessias,Nicoleta Economou-Zavlanos,Armando D. Bedoya,Monica Agrawal,Michael M. Zavlanos,Anand Chowdhury,Raj M. Ratwani,Kai Sun,Kathryn I. Pollak,Michael J. Pencina,Chuan Hong

{"title":"An agentic AI system for multi-framework communication coding","authors":"Bohao Yang,Rui Yang,Joshua M. Biro,Haoyuan Wang,Jessica L. Handley,Brianna N. Richardson,Sophia Bessias,Nicoleta Economou-Zavlanos,Armando D. Bedoya,Monica Agrawal,Michael M. Zavlanos,Anand Chowdhury,Raj M. Ratwani,Kai Sun,Kathryn I. Pollak,Michael J. Pencina,Chuan Hong","doi":"10.1038/s41746-026-03314-w","DOIUrl":"https://doi.org/10.1038/s41746-026-03314-w","url":null,"abstract":"Abstract Clinical communication is central to patient outcomes, yet large-scale annotation of patient–provider conversations remains labor-intensive, inconsistent, and difficult to scale. Existing large language model (LLM)–based approaches typically rely on single-task systems with limited robustness across communication frameworks and clinical domains. In this study, we developed MOSAIC, a M ulti-framew O rk S tructured Agentic AI system for scalable Clinical C ommunication coding. MOSAIC combines dynamically few-shot prompting, retrieval-augmented generation (RAG), and a LangGraph-based multi-agent workflow for planning, annotation, and verification. Performance was evaluated against gold-standard annotations produced by trained human coders. MOSAIC was developed on 54 annotated transcripts and evaluated on 120 transcripts spanning rheumatology and obstetrics/gynecology (OB/GYN). On the evaluation set, MOSAIC achieved an overall weighted F1 score of 95.6%. Performance was highest in rheumatology (F1 = 95.9%) and strongest for bias and patient behaviors categories (F1 = 96.4% and 96.1%, respectively). These results demonstrate the potential of multi-framework AI systems for reliable clinical communication analysis.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"160 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208622","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