NPJ Digital Medicine最新文献

Clinical decision support for vestibular diagnosis: large-scale machine learning with lived experience coaching 前庭诊断的临床决策支持：大规模机器学习与生活经验指导

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-31 DOI: 10.1038/s41746-025-01880-z

Cecilia A. Callejas Pastor, Hyun Tae Ryu, Jung Sook Joo, Yunseo Ku, Myung-Whan Suh

{"title":"Clinical decision support for vestibular diagnosis: large-scale machine learning with lived experience coaching","authors":"Cecilia A. Callejas Pastor, Hyun Tae Ryu, Jung Sook Joo, Yunseo Ku, Myung-Whan Suh","doi":"10.1038/s41746-025-01880-z","DOIUrl":"https://doi.org/10.1038/s41746-025-01880-z","url":null,"abstract":"Diagnosing vestibular disorders remains challenging due to complex symptoms and extensive history-taking required. While machine learning approaches have shown promise in medical diagnostics, their application to vestibular disorder classification has been limited. We developed a CatBoost machine learning model to classify six common vestibular disorders using a retrospective dataset of patients. The model incorporates 50 clinical features, selected through a hybrid approach combining algorithmic methods (RFE-SVM and SKB score) and expert clinical knowledge. We designed the system to achieve high sensitivity for common vestibular disorders (BPPV and VM) and high specificity for conditions requiring intensive interventions (MD and HOD) or careful differential diagnosis (PPPD and VEST) to minimize unnecessary invasive treatments. When applied to test data, reaches 88.4% accuracy, with 60.9% correct classifications, 27.5% partially correct, and 11.6% incorrect classifications. Results suggest that machine learning can support clinical decision-making in vestibular disorder diagnosis when combining algorithmic capabilities with clinical expertise.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"15 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144747488","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 artificial intelligence applications in clinical trial risk assessment 人工智能在临床试验风险评估中的应用范围综述

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-30 DOI: 10.1038/s41746-025-01886-7

Douglas Teodoro, Nona Naderi, Anthony Yazdani, Boya Zhang, Alban Bornet

{"title":"A scoping review of artificial intelligence applications in clinical trial risk assessment","authors":"Douglas Teodoro, Nona Naderi, Anthony Yazdani, Boya Zhang, Alban Bornet","doi":"10.1038/s41746-025-01886-7","DOIUrl":"https://doi.org/10.1038/s41746-025-01886-7","url":null,"abstract":"Artificial intelligence (AI) is increasingly applied to clinical trial risk assessment, aiming to improve safety and efficiency. This scoping review analyzed 142 studies published between 2013 and 2024, focusing on safety (n = 55), efficacy (n = 46), and operational (n = 45) risk prediction. AI techniques, including traditional machine learning, deep learning (e.g., graph neural networks, transformers), and causal machine learning, are used for tasks like adverse drug event prediction, treatment effect estimation, and phase transition prediction. These methods utilize diverse data sources, from molecular structures and clinical trial protocols to patient data and scientific publications. Recently, large language models (LLMs) have seen a surge in applications, featuring in 7 out of 33 studies in 2023. While some models achieve high performance (AUROC up to 96%), challenges remain, including selection bias, limited prospective studies, and data quality issues. Despite these limitations, AI-based risk assessment holds substantial promise for transforming clinical trials, particularly through improved risk-based monitoring frameworks.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"7 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144737740","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

Physics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA tests. 通过PSA测试重建前列腺癌肿瘤生长的物理信息机器学习数字双胞胎。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-29 DOI: 10.1038/s41746-025-01890-x

Daniel Camacho-Gomez,Carlos Borau,Jose Manuel Garcia-Aznar,Maria Jose Gomez-Benito,Mark Girolami,Maria Angeles Perez

引用次数: 0

Personalized home based neurostimulation via AI optimization augments sustained attention 通过人工智能优化的个性化家庭神经刺激增强了持续的注意力

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-29 DOI: 10.1038/s41746-025-01744-6

Roi Cohen Kadosh, Delia Ciobotaru, Malin I. Karstens, Vu Nguyen

引用次数: 0

Evaluation of performance of generative large language models for stroke care 脑卒中护理生成式大型语言模型的性能评价

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-29 DOI: 10.1038/s41746-025-01830-9

John Tayu Lee, Vincent Cheng-Sheng Li, Jia-Jyun Wu, Hsiao-Hui Chen, Sophia Sin-Yu Su, Brian Pin-Hsuan Chang, Richard Lee Lai, Chi-Hung Liu, Chung-Ting Chen, Valis Tanapima, Toby Kai-Bo Shen, Rifat Atun

{"title":"Evaluation of performance of generative large language models for stroke care","authors":"John Tayu Lee, Vincent Cheng-Sheng Li, Jia-Jyun Wu, Hsiao-Hui Chen, Sophia Sin-Yu Su, Brian Pin-Hsuan Chang, Richard Lee Lai, Chi-Hung Liu, Chung-Ting Chen, Valis Tanapima, Toby Kai-Bo Shen, Rifat Atun","doi":"10.1038/s41746-025-01830-9","DOIUrl":"https://doi.org/10.1038/s41746-025-01830-9","url":null,"abstract":"Stroke is a leading cause of global morbidity and mortality, disproportionately impacting lower socioeconomic groups. In this study, we evaluated three generative LLMs—GPT, Claude, and Gemini—across four stages of stroke care: prevention, diagnosis, treatment, and rehabilitation. Using three prompt engineering techniques—Zero-Shot Learning (ZSL), Chain of Thought (COT), and Talking Out Your Thoughts (TOT)—we applied each to realistic stroke scenarios. Clinical experts assessed the outputs across five domains: (1) accuracy; (2) hallucinations; (3) specificity; (4) empathy; and (5) actionability, based on clinical competency benchmarks. Overall, the LLMs demonstrated suboptimal performance with inconsistent scores across domains. Each prompt engineering method showed strengths in specific areas: TOT does well in empathy and actionability, COT was strong in structured reasoning during diagnosis, and ZSL provided concise, accurate responses with fewer hallucinations, especially in the Treatment stage. However, none consistently met high clinical standards across all stroke care stages.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"90 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144719385","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

Rapid vessel segmentation and reconstruction of head and neck angiograms from MR vessel wall images 基于MR血管壁图像的头颈部血管图像快速分割与重建

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-28 DOI: 10.1038/s41746-025-01866-x

Jin Zhang, Wen Wang, Jinhua Dong, Xiong Yang, Shuwei Bai, Jiaqi Tian, Bo Li, Xiao Li, Jianjian Zhang, Hangyu Wu, Xiaoxi Zeng, Yongqiang Ye, Shenghao Ding, Jieqing Wan, Ke Wu, Yufei Mao, Cheng Li, Na Zhang, Jianrong Xu, Yongming Dai, Feng Shi, Beibei Sun, Yan Zhou, Huilin Zhao

{"title":"Rapid vessel segmentation and reconstruction of head and neck angiograms from MR vessel wall images","authors":"Jin Zhang, Wen Wang, Jinhua Dong, Xiong Yang, Shuwei Bai, Jiaqi Tian, Bo Li, Xiao Li, Jianjian Zhang, Hangyu Wu, Xiaoxi Zeng, Yongqiang Ye, Shenghao Ding, Jieqing Wan, Ke Wu, Yufei Mao, Cheng Li, Na Zhang, Jianrong Xu, Yongming Dai, Feng Shi, Beibei Sun, Yan Zhou, Huilin Zhao","doi":"10.1038/s41746-025-01866-x","DOIUrl":"https://doi.org/10.1038/s41746-025-01866-x","url":null,"abstract":"Three-dimensional magnetic resonance vessel wall imaging (3D MR-VWI) is critical for characterizing cerebrovascular pathologies, yet its clinical adoption is hindered by labor-intensive postprocessing. We developed VWI Assistant, a multi-sequence integrated deep learning platform trained on multicenter data (study cohorts 1981 patients and imaging datasets) to automate artery segmentation and reconstruction. The framework demonstrated robust performance across diverse patient populations, imaging protocols, and scanner manufacturers, achieving 92.9% qualified rate comparable to expert manual delineation. VWI Assistant reduced processing time by over 90% (10–12 min per case) compared to manual methods (p < 0.001) and improved inter-/intra-reader agreement. Real-world deployment (n = 1099 patients) demonstrated rapid clinical adoption, with utilization rates increasing from 10.8% to 100.0% within 12 months. By streamlining 3D MR-VWI workflows, VWI Assistant addresses scalability challenges in vascular imaging, offering a practical tool for routine use and large-scale research, significantly improving workflow efficiency while reducing labor and time costs.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"4 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144715284","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 consensus statement on the use of digital twins in medicine 关于在医学中使用数字双胞胎的共识声明

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-28 DOI: 10.1038/s41746-025-01897-4

Jeffrey David Iqbal, Michael Krauthammer, Claudia M. Witt, Nikola Biller-Andorno, Markus Christen

引用次数: 0

Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson’s disease 多队列机器学习识别帕金森病认知障碍的预测因素

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-26 DOI: 10.1038/s41746-025-01862-1

Rebecca Ting Jiin Loo, Lukas Pavelka, Graziella Mangone, Fouad Khoury, Marie Vidailhet, Jean-Christophe Corvol, Enrico Glaab

引用次数: 0

AI-driven preclinical disease risk assessment using imaging in UK biobank 人工智能驱动的临床前疾病风险评估在英国生物银行成像

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-26 DOI: 10.1038/s41746-025-01771-3

Dmitrii Seletkov, Sophie Starck, Tamara T. Mueller, Yundi Zhang, Lisa Steinhelfer, Daniel Rueckert, Rickmer Braren

{"title":"AI-driven preclinical disease risk assessment using imaging in UK biobank","authors":"Dmitrii Seletkov, Sophie Starck, Tamara T. Mueller, Yundi Zhang, Lisa Steinhelfer, Daniel Rueckert, Rickmer Braren","doi":"10.1038/s41746-025-01771-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01771-3","url":null,"abstract":"Identifying disease risk and detecting disease before clinical symptoms appear are essential for early intervention and improving patient outcomes. In this context, the integration of medical imaging in a clinical workflow offers a unique advantage by capturing detailed structural and functional information. Unlike non-image data, such as lifestyle, sociodemographic, or prior medical conditions, which often rely on self-reported information susceptible to recall biases and subjective perceptions, imaging offers more objective and reliable insights. Although the use of medical imaging in artificial intelligence (AI)-driven risk assessment is growing, its full potential remains underutilized. In this work, we demonstrate how imaging can be integrated into routine screening workflows, in particular by taking advantage of neck-to-knee whole-body magnetic resonance imaging (MRI) data available in the large prospective study UK Biobank. Our analysis focuses on three-year risk assessment for a broad spectrum of diseases, including cardiovascular, digestive, metabolic, inflammatory, degenerative, and oncologic conditions. We evaluate AI-based pipelines for processing whole-body MRI and demonstrate that using image-derived radiomics features provides the best prediction performance, interpretability, and integration capability with non-image data.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"29 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144712316","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 role of face regions in remote photoplethysmography for contactless heart rate monitoring 面部区域在非接触式心率监测的远程光电容积脉搏图中的作用

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-26 DOI: 10.1038/s41746-025-01814-9

Maksym Bondarenko, Carlo Menon, Mohamed Elgendi

引用次数: 0