NPJ Digital Medicine最新文献

Unsupervised machine learning for placental disease using cell spatial organization 利用细胞空间组织进行胎盘疾病的无监督机器学习

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-09-05 DOI: 10.1038/s41746-026-03177-1

Mangalam Sahai,Liron Pantanowitz,Philip LeDuc,Jonathan Cagan

引用次数: 0

Exploring generalizability and explainability of LLMs in classifying clinically rated suicidal ideation using heterogeneous data 利用异质数据探讨llm分类临床评定自杀意念的普遍性和可解释性

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-09-05 DOI: 10.1038/s41746-026-03198-w

Rong Huang,Longdi Xian,Christopher Chi Wai Cheng,Jie Chen,Kit Ying Chan,Calvin Lam,Joey W. Y. Chan,Steven W. H. Chau,Ngan Yin Chan,Bei Huang,Yun Kwok Wing,Tim M. H. Li

{"title":"Exploring generalizability and explainability of LLMs in classifying clinically rated suicidal ideation using heterogeneous data","authors":"Rong Huang,Longdi Xian,Christopher Chi Wai Cheng,Jie Chen,Kit Ying Chan,Calvin Lam,Joey W. Y. Chan,Steven W. H. Chau,Ngan Yin Chan,Bei Huang,Yun Kwok Wing,Tim M. H. Li","doi":"10.1038/s41746-026-03198-w","DOIUrl":"https://doi.org/10.1038/s41746-026-03198-w","url":null,"abstract":"Abstract While artificial intelligence (AI) and large language models (LLMs) have shown promise in identifying and classifying suicidal ideation, their generalizability and equity in the presence of heterogeneous clinical data remain largely unexplored. This study hypothesized a subgroup disparity in a crude AI classifier of clinician-rated suicidal ideation because of the linguistic heterogeneity and proposed a factorization approach to decompose complex data into simpler components by reducing topic dimensions of clinical transcripts. Results showed that topic-specific classifiers reduced subgroup disparity compared to the topic-general classifier, with ΔAUC decreasing from 0.11 to 0.01 and 0.05—a noticeable reduction of 0.10 and 0.06, respectively. More specifically, with the topic-general classifier, the odds of missing a suicidal case increased by 2.39 times for alexithymia individuals, compared to non-alexithymia individuals (OR = 2.39, p = 0.002). These findings underscore the significance of data heterogeneity on AI classifiers of suicidal ideation and demonstrate the potential of the proposed factorization approach.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"71 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893701","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

Senescence-aware filtering facilitates robust prediction of cancer immunotherapy outcomes 衰老感知过滤有助于癌症免疫治疗结果的稳健预测

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-09-05 DOI: 10.1038/s41746-026-03131-1

Minsoo Kim,Woomin Song,Hyunsoo Ahn,Giljae Chung,Sanguk Kim

引用次数: 0

From feasibility to neuroanatomic validity of remote cognitive smartphone assessments in early Alzheimer’s disease 从早期阿尔茨海默病远程认知智能手机评估的可行性到神经解剖学有效性

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-09-05 DOI: 10.1038/s41746-026-03108-0

Kirsten I. Taylor,Arnaud M. Wolfer,Irma T. Kurniawan,Miguel Veloso,Goullou Keita,Niels Hagenbuch,Beijue Shi,Foteini Orfaniotou,Eduardo A. Aponte,Macarena Garcia Valdecasas Colell,Christopher H. Chatham,Štefan Holiga,Raphael Ullmann,Wael Abouelkheir,Simone Rey-Riek,Emma Poon,David Watson,Mercè Boada,Thanneer M. Perumal

引用次数: 0

Platforms for artificial intelligence-enabled infectious disease surveillance 人工智能支持的传染病监测平台

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-09-04 DOI: 10.1038/s41746-026-03189-x

Marya Getchell,Michael Barber,Thomas Carpino,Carl J. E. Suster,Vitali Sintchenko,Nan Liu,Suci Wulandari,Yoong Khean Khoo,Ashley Tsai,Junxiong Pang,David B. Hipgrave,Shurendar Kumar,Ahmad Watsiq Maula,Philip AbdelMalik,Timothy J. Dallman,Paul M. Pronyk

引用次数: 0

A voice-biomarker foundation model for ALS monitoring and Parkinson’s screening 用于ALS监测和帕金森筛查的语音生物标志物基础模型

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-09-04 DOI: 10.1038/s41746-026-03206-z

Arturo Loaiza-Bonilla,Pranav Arora,Nathan Hayman,Andres Deik

引用次数: 0

The illusion of clinical reasoning: a benchmark reveals the pervasive gap in vision-language models for clinical competency 临床推理的幻觉：一个基准揭示了临床能力的视觉语言模型的普遍差距

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-09-03 DOI: 10.1038/s41746-026-03191-3

Dingyu Wang,Zimu Yuan,Jiajun Liu,Shanggui Liu,Nan Zhou,Tianxing Xu,Di Huang,Dong Jiang

引用次数: 0

A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence. 诊断和预测医疗人工智能的五阶段评估框架。

IF 18 1区医学

NPJ Digital Medicine Pub Date : 2026-09-03 DOI: 10.1038/s41746-026-03155-7

Zichen Ye, Yue Chen, Xuefeng Huang, Manman Chen, Wanzhou Wang, He Zhu, Keyu Han, Jiahui Wang, Qu Lu, Yuankai Zhao, Yimin Qu, Guanghan Gao, Yu Jiang

{"title":"A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence.","authors":"Zichen Ye, Yue Chen, Xuefeng Huang, Manman Chen, Wanzhou Wang, He Zhu, Keyu Han, Jiahui Wang, Qu Lu, Yuankai Zhao, Yimin Qu, Guanghan Gao, Yu Jiang","doi":"10.1038/s41746-026-03155-7","DOIUrl":"https://doi.org/10.1038/s41746-026-03155-7","url":null,"abstract":"<p><p>Artificial intelligence (AI) has advanced rapidly across diagnostic, prognostic, and clinical decision-support applications, yet the pathway from laboratory performance to demonstrable clinical benefit remains fragmented and inconsistently defined. Existing evaluations rely heavily on retrospective testing and algorithm-centric metrics, while current guidelines emphasize reporting standards rather than specifying validation across stages of model maturity. This study proposes a five-phase evaluation framework for medical AI, supported by a dynamic evaluation architecture reflecting the nonlinear, iterative nature of AI systems. The framework integrates technical validation, operational robustness validation, controlled interaction validation, clinical evidence validation, and real-world integration validation, while incorporating phase-gating criteria and local and systemic fall-back triggers. These mechanisms enable re-entry into earlier phases based on drift, version updates, or safety signals, and accommodate parallel activities such as implementation research informing clinical trials. By systematically mapping multicenter external validation, shadow-mode testing, human-AI comparison and cooperation studies, randomized controlled trials, real-world evaluations, and adaptive designs into a coherent lifecycle pathway, the framework addresses persistent gaps between laboratory performance and clinical benefit. It provides researchers, clinical institutions, and regulators with an operational, scalable approach aligned with evolving regulatory expectations, supporting trustworthy, ethically aligned, and lifecycle-based evidence generation for medical AI systems.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"9 1","pages":""},"PeriodicalIF":18.0,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148891789","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

Explicit chain of diagnosis for CMR semantic interpretation via large reasoning model 基于大型推理模型的CMR语义解释显式诊断链

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-09-03 DOI: 10.1038/s41746-026-03175-3

YanSong Li,JinYu Zheng,Yun Tian,ShiFeng Zhao,ChongWen Wu,DongAoLei An,Jie Du,Meng Li,ZhongXi Qiu,LianMing Wu,Shuo Li

引用次数: 0

Differences in tone of AI and care team responses to patient messages by patient demographics 人工智能的语气和护理团队对患者信息的反应的差异

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2026-09-03 DOI: 10.1038/s41746-026-03185-1

Angela Mastrianni,William R. Small,Yuhan Betty Cui,Amelia Shunk,Soumik Mandal,Simon Jones,Nicole Redfern,Adam Szerencsy,Yindalon Aphinyanaphongs,Safiya Richardson

引用次数: 0