NPJ Digital Medicine最新文献_第2页

Automated differentiation for parkinsonism using clinical structural MRI: A multicenter study 临床结构MRI对帕金森病的自动鉴别：一项多中心研究

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-16 DOI: 10.1038/s41746-026-03357-z

Liche Zhou,Chengzhe Wang,Xue Zhu,Dongling Zhang,Jinfeng Wu,Sijia Huang,Haiying Lyu,Chuanying Xu,Zihao Lin,Xiaoyu Cheng,Jinru Zhang,Chengjie Mao,Chunfeng Liu,Yong Lu,Guiyun Cui,Tao Wu,Kezhong Zhang,Ailian Du,Jun Liu,Jie Wang

引用次数: 0

Sycophantic AI, critical-feedback tolerance, and the case for productive friction in medical training 阿谀奉承的人工智能，关键反馈容忍，以及医疗培训中有效摩擦的案例

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-16 DOI: 10.1038/s41746-026-03389-5

Zhi Chen,Bo Jia,Kangxiang Ji,Huilin Sheng,Mengyun Sui,Long Xue

引用次数: 0

A generalist model for diverse text-guided medical image synthesis 多种文本引导医学图像合成的通才模型

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-16 DOI: 10.1038/s41746-026-03381-z

Joseph Cho,Mrudang Mathur,Cyril Zakka,Dhamanpreet Kaur,Matthew Leipzig,Kelly Higa,Alex Dalal,Aravind Krishnan,Eubee Koo,Karen Wai,Cindy S. Zhao,Akshay Chaudhari,Matthew Duda,Ashley Choi,Ehsan Rahimy,Lyna Azzouz,Robyn Fong,Rohan Shad,William Hiesinger

引用次数: 0

Characterizing time-of-day physical activity patterns across cognitive trajectories in older adults: a longitudinal cohort study 在老年人的认知轨迹中表征一天中的身体活动模式：一项纵向队列研究

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-15 DOI: 10.1038/s41746-026-03370-2

Sunan Gao,Ciprian M. Crainiceanu,Anis Davoudi,Ryan J. Dougherty,Lacey Etzkorn,Priya Palta,Amal A. Wanigatunga,Adam P. Spira,Jennifer A. Schrack

引用次数: 0

Solving operating room scheduling problems using large language model based multi-agent architecture 基于多智能体结构的大语言模型求解手术室调度问题

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-15 DOI: 10.1038/s41746-026-03319-5

Chu Tang,Qu Wei,Haitao Liu,Zekun Jiang,Xiaobo Zhou,Yu Cao,Kang Li

引用次数: 0

Implementation and effectiveness of a smartphone app targeted at improving physicians adherence to antimicrobial treatment guidelines 智能手机应用程序的实施和有效性，旨在提高医生对抗菌药物治疗指南的依从性

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-15 DOI: 10.1038/s41746-026-03401-y

Minyahil Tadesse Boltena,Mirkuzie Woldie,Alemseged Abdissa,Ziad El-Khatib,Yibeltal Siraneh,Abraham Sahilemichael Kebede,Wendosen Shewarega,Zelalem Tadesse,Wondwossen Amanuel,Getachew Tesfaye Beyene,Kassa Haile,Sudhakar Morankar

{"title":"Implementation and effectiveness of a smartphone app targeted at improving physicians adherence to antimicrobial treatment guidelines","authors":"Minyahil Tadesse Boltena,Mirkuzie Woldie,Alemseged Abdissa,Ziad El-Khatib,Yibeltal Siraneh,Abraham Sahilemichael Kebede,Wendosen Shewarega,Zelalem Tadesse,Wondwossen Amanuel,Getachew Tesfaye Beyene,Kassa Haile,Sudhakar Morankar","doi":"10.1038/s41746-026-03401-y","DOIUrl":"https://doi.org/10.1038/s41746-026-03401-y","url":null,"abstract":"Abstract The effectiveness of mHealth interventions for improving antimicrobial guideline adherence remains insufficiently understood in resource-limited settings. This study evaluated a context-adapted smartphone Standard Treatment Guideline (STG) app designed to improve physicians’ adherence to antimicrobial treatment guidelines in Ethiopia. A type 1 hybrid effectiveness–implementation cluster-randomized trial was conducted in 20 hospitals randomized to a smartphone app plus training or paper-based guidelines and retrospectively registered (PACTR202504495116733). The primary outcome was guideline-adherent prescribing, defined as correct antibiotic selection, dose, frequency, and duration when treatment was indicated, or appropriately documented non-prescribing when antibiotics were not indicated across 1433 patient charts. Secondary outcomes included overall antibiotic use and broad-spectrum antibiotic use. Guideline adherence improved by 26.7 percentage points in crude difference-in-differences analysis, and the adjusted intervention-by-endline effect showed higher odds of guideline-adherent prescribing (aOR 3.89; 95% CI: 1.96–7.73). Overall, antibiotic use declined from 96.9% to 82.2% in the intervention arm. In a post-hoc sensitivity analysis using all eligible charts, broad-spectrum antibiotic use declined from 83.9 to 57.5%. These findings support the potential value of a multicomponent intervention combining an offline STG app with training and implementation support for improving guideline-concordant antimicrobial prescribing; however, the independent contribution of the mobile application cannot be determined.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"406 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208646","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

An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models 基于大型语言模型的临床记录端到端罕见病表型分析的人工智能框架

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-15 DOI: 10.1038/s41746-026-03275-0

Cathy Shyr,Yan Hu,Rory J. Tinker,Thomas A. Cassini,Kevin W. Byram,Rizwan Hamid,Daniel V. Fabbri,Adam Wright,Josh F. Peterson,Undiagnosed Diseases Network,Lisa Bastarache,Hua Xu

{"title":"An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models","authors":"Cathy Shyr,Yan Hu,Rory J. Tinker,Thomas A. Cassini,Kevin W. Byram,Rizwan Hamid,Daniel V. Fabbri,Adam Wright,Josh F. Peterson,Undiagnosed Diseases Network,Lisa Bastarache,Hua Xu","doi":"10.1038/s41746-026-03275-0","DOIUrl":"https://doi.org/10.1038/s41746-026-03275-0","url":null,"abstract":"Phenotyping is fundamental to rare disease diagnosis, but manually curating structured phenotypes from clinical notes is labor intensive and difficult to scale. Existing artificial intelligence (AI) approaches optimize individual components of phenotyping but generally do not operationalize the full workflow of extracting features from clinical text, standardizing them to Human Phenotype Ontology (HPO) terms, and prioritizing rare disease-relevant HPO terms. We developed RARE-PHENIX, an end-to-end AI framework for rare disease phenotyping that integrates large language model-based phenotype extraction, ontology-grounded standardization to HPO terms, and supervised ranking of HPO terms. We trained RARE-PHENIX using data from 2671 patients across 11 Undiagnosed Diseases Network clinical sites, and externally validated it on 16,357 real-world clinical notes from 143 patients at Vanderbilt University Medical Center. Our findings suggest RARE-PHENIX generates structured, prioritized HPO terms highly concordant with clinician curation, demonstrates potential downstream disease-prioritization signal, and may support human-in-the-loop rare disease phenotyping in real-world settings.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"168 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208655","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

TrajOnco: a multi-agent framework for temporal reasoning over longitudinal EHR for multi-cancer early detection TrajOnco：用于多种癌症早期检测的纵向电子病历时间推理的多智能体框架

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-15 DOI: 10.1038/s41746-026-03377-9

Sihang Zeng,Young Won Kim,Wilson Lau,Ehsan Alipour,Ruth Etzioni,Meliha Yetisgen,Anand Oka

{"title":"TrajOnco: a multi-agent framework for temporal reasoning over longitudinal EHR for multi-cancer early detection","authors":"Sihang Zeng,Young Won Kim,Wilson Lau,Ehsan Alipour,Ruth Etzioni,Meliha Yetisgen,Anand Oka","doi":"10.1038/s41746-026-03377-9","DOIUrl":"https://doi.org/10.1038/s41746-026-03377-9","url":null,"abstract":"Abstract Accurate estimation of cancer risk from longitudinal electronic health records (EHRs) could support earlier detection and improved care, but modeling such complex patient trajectories remains challenging. We present TrajOnco, a training-free, multi-agent large language model (LLM) framework designed for scalable multi-cancer early detection. Using a chain-of-agents architecture with long-term memory, TrajOnco performs temporal reasoning over sequential clinical events to generate patient-level summaries, evidence-linked rationales, and predicted risk scores. We evaluated TrajOnco on de-identified Truveta EHR data across 15 cancer types using matched case-control cohorts, predicting risk of cancer diagnosis at 1 year. In zero-shot evaluation, TrajOnco achieved AUROCs of 0.64–0.80, performing comparably to supervised machine learning in a lung cancer benchmark while demonstrating better temporal reasoning than single-agent LLMs. The multi-agent design also enabled effective temporal reasoning with smaller-capacity models such as GPT-4.1-mini. The groundedness of TrajOnco’s output was supported by human evaluation. Furthermore, TrajOnco’s interpretable reasoning outputs can be aggregated to reveal population-level risk patterns that align with established clinical knowledge. These findings highlight the potential of multi-agent LLMs to execute interpretable temporal reasoning over longitudinal EHRs, advancing both scalable multi-cancer early detection and clinical insight generation.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"11 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208656","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

Current status and future outlook of artificial intelligence and automation in intravenous infusion systems. 人工智能与自动化在静脉输液系统中的应用现状与展望。

IF 18 1区医学

NPJ Digital Medicine Pub Date : 2026-10-14 DOI: 10.1038/s41746-026-03291-0

Islam M Ibrahim, Rahma A Elkomy, Merna A Mohamed, Fatemah A Farok, Roaa E Emam, Salma A Kamel, Reham M Asem

{"title":"Current status and future outlook of artificial intelligence and automation in intravenous infusion systems.","authors":"Islam M Ibrahim, Rahma A Elkomy, Merna A Mohamed, Fatemah A Farok, Roaa E Emam, Salma A Kamel, Reham M Asem","doi":"10.1038/s41746-026-03291-0","DOIUrl":"10.1038/s41746-026-03291-0","url":null,"abstract":"<p><p>The combination of the Internet of Things (IoT), artificial intelligence (AI), and wearable biosensors is transforming healthcare by allowing continuous, real-time monitoring and more intelligent clinical decision-making. This review explores AI-driven monitoring, smart intravenous (IV) infusion automation, and wearable biosensors, and proposes Intelligent IV Automation and Smart Patient Synchronization (INFUSYNC), a conceptual framework synchronizing monitoring, automated fluid delivery, and predictive analytics. The literature shows meaningful progress: wearables increasingly support non-contact monitoring with near clinical-grade accuracy for select parameters, while infusion pumps with dose-error-reduction and AI-assisted decision support are reported to improve delivery precision and safety. Predictive models further support early detection of physiological decline. Progress remains uneven, however: most AI-enabled infusion systems are prototype-stage, closed-loop control remains rare, and interoperability, data security, and privacy remain underdeveloped. Future systems will need compatible, security-by-design, clinically validated, feedback-controlled architectures. Overall, this review outlines a clear but early-stage trajectory toward integrated smart-health environments for IV therapy.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"9 1","pages":""},"PeriodicalIF":18.0,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13669359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149183290","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

Sequence models reveal diagnosis accumulation pathways beyond comorbidity burden in population-scale hospital data 序列模型揭示了在人口规模的医院数据中超越合并症负担的诊断积累途径

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-10-14 DOI: 10.1038/s41746-026-03265-2

Katharina Ledebur,Mitja Devetak,Peter Klimek

引用次数: 0