NPJ Digital Medicine最新文献_第8页

Payment models in pediatric digital health and their implications for innovation, policy, and access 儿科数字健康的支付模式及其对创新、政策和获取的影响

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-08 DOI: 10.1038/s41746-026-03315-9

Aydin Zahedivash,Ryan CL Brewster,Meagan F. Moyer,Brian Han,Anuradha Dayal,Benjamin S. Glicksberg,Michelle de Haaff,Oliver Aalami,Sandra Waugh Ruggles,Lindsay A. Stevens,Chethan Sarabu

引用次数: 0

Critical gaps in health technology assessment guidance for generative AI across six health systems 6个卫生系统对生成式人工智能的卫生技术评估指导存在重大差距

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-08 DOI: 10.1038/s41746-026-03365-z

Robin van Kessel,Jelena Schmidt,Stephanie Winitsky,George Wharton,Alexander W. Carter,Chaohui Guo,Michael King,Afua van Haasteren,Okan Ekinci,Jochen Klucken,Elias Mossialos

{"title":"Critical gaps in health technology assessment guidance for generative AI across six health systems","authors":"Robin van Kessel,Jelena Schmidt,Stephanie Winitsky,George Wharton,Alexander W. Carter,Chaohui Guo,Michael King,Afua van Haasteren,Okan Ekinci,Jochen Klucken,Elias Mossialos","doi":"10.1038/s41746-026-03365-z","DOIUrl":"https://doi.org/10.1038/s41746-026-03365-z","url":null,"abstract":"Abstract The integration of artificial intelligence (AI) in healthcare poses significant challenges for evaluation due to algorithmic complexity and evolving performance. Existing evidence generation guidelines, primarily designed for static medical technologies, may inadequately address the unique characteristics of generative AI. Here, we conducted a comparative analysis of evidence generation guidelines from six countries, examining recommendations for efficacy and effectiveness evaluations, health economic evaluations, social and equity evaluations, and uncertainty management for AI technologies. Across 56 documents, we found a consistent preference for randomised controlled trials and cost-effectiveness analyses, alongside emerging acceptance of alternative study designs and real-world evidence to accommodate the evolving nature of AI technologies. However, current frameworks lack specific guidance for the stochastic, non-deterministic, and adaptive learning properties of generative AI technologies. Moving forward, evidence generation guidelines require methodological and conceptual adaptations in order to improve the value assessment for generative AI technologies.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"37 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149150059","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

Adaptive heterogeneous graph neural networks for differentiating pulmonary arterial hypertension from left heart disease 自适应异质图神经网络鉴别肺动脉高压与左心疾病

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-08 DOI: 10.1038/s41746-026-03305-x

Yingran Shen,Jie Mi,Xiaogang Zhao,Gongzhe Liu,Guang Yang,Bei Yang,Hao Wang,Yijiu Ren,Jie Cai

{"title":"Adaptive heterogeneous graph neural networks for differentiating pulmonary arterial hypertension from left heart disease","authors":"Yingran Shen,Jie Mi,Xiaogang Zhao,Gongzhe Liu,Guang Yang,Bei Yang,Hao Wang,Yijiu Ren,Jie Cai","doi":"10.1038/s41746-026-03305-x","DOIUrl":"https://doi.org/10.1038/s41746-026-03305-x","url":null,"abstract":"Abstract Differentiating pulmonary arterial hypertension (PAH) from pulmonary hypertension associated with left heart disease (PH-LHD) is clinically important because management differs substantially, but definitive classification requires invasive haemodynamic assessment together with clinical evaluation. We retrospectively studied 905 patients with PAH or PH-LHD treated at Shanghai Pulmonary Hospital and developed an adaptive heterogeneous graph neural network (AHGNN) that integrates contrast-enhanced thoracic CT images with noninvasive clinical variables. The model combines differentiable graph construction using Gumbel-Softmax reparameterization, hierarchical cross-modal attention, and dual-level self-supervised contrastive learning. Across 100 outer test folds from repeated patient-level five-fold cross-validation, AHGNN achieved a mean area under the receiver operating characteristic curve (AUC) of 0.946 ± 0.023 and a precision–recall AUC of 0.952 ± 0.009. At a fixed probability threshold of 0.50, sensitivity was 0.867 ± 0.070 and specificity was 0.867 ± 0.072. The Brier score was 0.139, although a calibration slope of 3.547 indicated that recalibration may be required. These findings support the potential of multimodal noninvasive data to assist referral and diagnostic triage. Prospective external validation, recalibration, and clinical safety evaluation are required before clinical use or any change to the role of right heart catheterization.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"53 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149150055","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

Governing cognitive labor delegated to AI in scholarly writing and medical education 在学术写作和医学教育中，人工智能管理认知劳动

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-08 DOI: 10.1038/s41746-026-03374-y

Zhicheng Lin

引用次数: 0

From quantitative features to imaging signs: agentic AI-driven autonomous discovery in glioblastoma 从定量特征到影像学征象：人工智能驱动的胶质母细胞瘤自主发现

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-07 DOI: 10.1038/s41746-026-03331-9

Qian Li, Yan Liu, Biao Jiang, Fei Dong

{"title":"From quantitative features to imaging signs: agentic AI-driven autonomous discovery in glioblastoma","authors":"Qian Li, Yan Liu, Biao Jiang, Fei Dong","doi":"10.1038/s41746-026-03331-9","DOIUrl":"https://doi.org/10.1038/s41746-026-03331-9","url":null,"abstract":"The development of semantic imaging signs is time-consuming and relies heavily on clinicians’ experiential knowledge, whereas the abundance of quantitative radiomic features often poses challenges in interpretability and clinical translation. We developed an agentic AI pipeline to autonomously translate quantitative morphological features into semantic signs. The pipeline autonomously performed end-to-end processing—from descriptive statistical profiling and semantic sign translation to visualization. In a development dataset of 106 supratentorial glioblastoma (GBM) cases, the pipeline generated eight candidate signs; the three most consistently proposed signs were the cauliflower, eggplant, and pancake signs. Two radiologists independently validated the cauliflower sign on an external cohort of 50 GBMs and 50 metastases, achieving AUCs of 0.73 (95% CI: 0.64–0.82) and 0.77 (95% CI: 0.69–0.85), respectively (mean 0.75) for tumor differentiation. These findings demonstrate that agentic AI can directly derive semantic imaging signs from quantitative features, offering a systematic and data-driven framework for sign discovery that may enhance the interpretability and clinical translation of radiomics.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"58 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087700","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

Artificial intelligence for the data-driven diagnosis of ADHD: a systematic review and meta-analysis 人工智能对ADHD的数据驱动诊断：系统回顾和荟萃分析

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-07 DOI: 10.1038/s41746-026-03289-8

Leilei Ma,Yujie Cao,Jingyan Chen,Ningyu Liu,Jiali Wang,Charlotte Walton,Yanpei Wang

引用次数: 0

Large language models for digital mental health: an HCI-centered scoping review 数字心理健康的大型语言模型：以hci为中心的范围审查

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-07 DOI: 10.1038/s41746-026-03317-7

Min Zeng,Zaifu Zhan,Kai Yu,Chao Zeng,Duzhen Zhang,Shuang Zhou,Yu Hou,Mingquan Lin,Rui Zhang

引用次数: 0

Participant-reported minimum acceptable sensitivity and specificity for artificial intelligence-based disease detection 参与者报告的基于人工智能的疾病检测的最低可接受灵敏度和特异性

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-07 DOI: 10.1038/s41746-026-03335-5

Jonghan Yu,Jiseon Lee,Mieun Kim,Hyeonjin Cho,Juhee Cho,Dong Wook Shin,Danbee Kang,Hong Kwan Kim

引用次数: 0

A generalizable biopsychosocial risk score for stratifying disease vulnerability in healthy populations 一种在健康人群中分层疾病易感性的可推广的生物心理社会风险评分

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-07 DOI: 10.1038/s41746-026-03316-8

Jin Chen,Congying Chu,Miguel Garcia-Argibay,Wei Li,Christos Christogiannis,Tianye Jia,Charlotte Walton,Sangma Xie,Tifei Yuan,Samuele Cortese,Bing Liu,Jiaojian Wang

引用次数: 0

Real-world user interactions with large language models for explaining home electrocardiogram reports 真实世界的用户交互与大型语言模型解释家庭心电图报告

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-07 DOI: 10.1038/s41746-026-03330-w

Shun Huang,Rongchen Lu,Junxian Lin,Gongzheng Tang,Chenyang He,Guangkun Nie,Yujie Xiao,Xiaocheng Fang,Deyun Zhang,Shuang Wei,Man Wu,Shenda Hong

引用次数: 0