NPJ Digital Medicine最新文献_第2页

Digital twins for the era of personalized surgery 个性化手术时代的数字双胞胎

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-05-15 DOI: 10.1038/s41746-025-01575-5

Yosra Magdi Mekki, Gijs Luijten, Elisabet Hagert, Sirajeddin Belkhair, Chris Varghese, Junaid Qadir, Barry Solaiman, Muhammad Bilal, Jaghtar Dhanda, Jan Egger, Jun Deng, Vikas Khanduja, Alejandro F. Frangi, Susu M. Zughaier, Mitchell A. Stotland

引用次数: 0

A review on generative AI models for synthetic medical text, time series, and longitudinal data 合成医学文本、时间序列和纵向数据的生成式人工智能模型综述

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-05-15 DOI: 10.1038/s41746-024-01409-w

Mohammad Loni, Fatemeh Poursalim, Mehdi Asadi, Arash Gharehbaghi

引用次数: 0

A quantitative analysis of the use of anonymization in biomedical research 生物医学研究中匿名化使用的定量分析

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-05-14 DOI: 10.1038/s41746-025-01644-9

Thierry Meurers, Karen Otte, Hammam Abu Attieh, Farah Briki, Jérémie Despraz, Mehmed Halilovic, Bayrem Kaabachi, Vladimir Milicevic, Armin Müller, Grigorios Papapostolou, Felix Nikolaus Wirth, Jean Louis Raisaro, Fabian Prasser

{"title":"A quantitative analysis of the use of anonymization in biomedical research","authors":"Thierry Meurers, Karen Otte, Hammam Abu Attieh, Farah Briki, Jérémie Despraz, Mehmed Halilovic, Bayrem Kaabachi, Vladimir Milicevic, Armin Müller, Grigorios Papapostolou, Felix Nikolaus Wirth, Jean Louis Raisaro, Fabian Prasser","doi":"10.1038/s41746-025-01644-9","DOIUrl":"https://doi.org/10.1038/s41746-025-01644-9","url":null,"abstract":"Anonymized biomedical data sharing faces several challenges. This systematic review analyzes 1084 PubMed-indexed studies (2018–2022) using anonymized biomedical data to quantify usage trends across geographic, regulatory, and cultural regions to identify effective approaches and inform implementation agendas. We identified a significant yearly increase in such studies with a slope of 2.16 articles per 100,000 when normalized against the total number of PubMed-indexed articles (p = 0.021). Most studies used data from the US, UK, and Australia (78.2%). This trend remained when normalized by country-specific research output. Cross-border sharing was rare (10.5% of studies). We identified twelve common data sources, primarily in the US (seven) and UK (three), including commercial (seven) and public entities (five). The prevalence of anonymization in the US, UK, and Australia suggests their practices could guide broader adoption. Rare cross-border anonymized data sharing and differences between countries with comparable regulations underscore the need for global standards.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"27 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143945750","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

Aligning AI principles and healthcare delivery organization best practices to navigate the shifting regulatory landscape 调整人工智能原则和医疗保健服务组织的最佳实践，以应对不断变化的监管环境

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-05-14 DOI: 10.1038/s41746-025-01605-2

Alifia Hasan, Noah Prizant, Jee Young Kim, Shreya Rao, David Vidal, Keo Shaw, Danny Tobey, Alexandra Valladares, Shira Zilberstein, Manesh Patel, Suresh Balu, Mark Sendak, Mark Lifson

引用次数: 0

Lessons from Henrietta Lacks inform a transparency framework to catalyze generative artificial intelligence in medicine 亨丽埃塔·拉克斯（Henrietta Lacks）的经验教训为促进医学中生成人工智能的透明度框架提供了信息

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-05-14 DOI: 10.1038/s41746-025-01656-5

Tej D. Azad, Anmol Warman, Deven McGraw, Suchi Saria

引用次数: 0

Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy 通过体内共聚焦显微镜诊断神经性角膜疼痛的深度学习模型的开发和验证

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-05-14 DOI: 10.1038/s41746-025-01577-3

Neslihan Dilruba Koseoglu, Eric Chen, Rudraksh Tuwani, Benjamin Kompa, Stephanie M. Cox, M. Cuneyt Ozmen, Mina Massaro-Giordano, Andrew L. Beam, Pedram Hamrah

{"title":"Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy","authors":"Neslihan Dilruba Koseoglu, Eric Chen, Rudraksh Tuwani, Benjamin Kompa, Stephanie M. Cox, M. Cuneyt Ozmen, Mina Massaro-Giordano, Andrew L. Beam, Pedram Hamrah","doi":"10.1038/s41746-025-01577-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01577-3","url":null,"abstract":"Neuropathic corneal pain (NCP) is an underdiagnosed ocular disorder caused by aberrant nociception and hypersensitivity of corneal nerves, often resulting in chronic pain and discomfort even in the absence of noxious stimuli. Recently, microneuromas (aberrant growth and swelling of the corneal nerve endings) detected using in vivo confocal microscopy (IVCM) have emerged as a promising biomarker for NCP. However, this process is time-intensive and error-prone, limiting its clinical use and availability. In this work, we present a new NCP screening system based on a deep learning model trained to detect microneuromas using a multisite dataset with a combined total of 103,168 IVCM images. Our model showed excellent discriminative ability detecting microneuromas (AuROC: 0.97) and the ability to generalize to data from a new institution (AuROC: 0.90). Additionally, our pipeline provides an uncertainty quantification mechanism that allows it to communicate when its predictions are reliable, further increasing its clinical relevance.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"25 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946098","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 framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation 评估llm医学文本摘要的临床安全性和幻觉率的框架

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-05-13 DOI: 10.1038/s41746-025-01670-7

Elham Asgari, Nina Montaña-Brown, Magda Dubois, Saleh Khalil, Jasmine Balloch, Joshua Au Yeung, Dominic Pimenta

{"title":"A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation","authors":"Elham Asgari, Nina Montaña-Brown, Magda Dubois, Saleh Khalil, Jasmine Balloch, Joshua Au Yeung, Dominic Pimenta","doi":"10.1038/s41746-025-01670-7","DOIUrl":"https://doi.org/10.1038/s41746-025-01670-7","url":null,"abstract":"Integrating large language models (LLMs) into healthcare can enhance workflow efficiency and patient care by automating tasks such as summarising consultations. However, the fidelity between LLM outputs and ground truth information is vital to prevent miscommunication that could lead to compromise in patient safety. We propose a framework comprising (1) an error taxonomy for classifying LLM outputs, (2) an experimental structure for iterative comparisons in our LLM document generation pipeline, (3) a clinical safety framework to evaluate the harms of errors, and (4) a graphical user interface, CREOLA, to facilitate these processes. Our clinical error metrics were derived from 18 experimental configurations involving LLMs for clinical note generation, consisting of 12,999 clinician-annotated sentences. We observed a 1.47% hallucination rate and a 3.45% omission rate. By refining prompts and workflows, we successfully reduced major errors below previously reported human note-taking rates, highlighting the framework’s potential for safer clinical documentation.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"8 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143945751","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

Patient perceptions of empathy in physician and artificial intelligence chatbot responses to patient questions about cancer. 病人对医生同理心的感知和人工智能聊天机器人对病人癌症问题的回应。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-05-13 DOI: 10.1038/s41746-025-01671-6

David Chen,Kabir Chauhan,Rod Parsa,Zhihui Amy Liu,Fei-Fei Liu,Ernie Mak,Lawson Eng,Breffni Louise Hannon,Jennifer Croke,Andrew Hope,Nazanin Fallah-Rad,Phillip Wong,Srinivas Raman

引用次数: 0

Wearable fNIRS platform for dense sampling and precision functional neuroimaging 用于密集采样和精密功能神经成像的可穿戴fNIRS平台

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-05-13 DOI: 10.1038/s41746-025-01690-3

Ali Rahimpour Jounghani, Anupam Kumar, Laura Moreno Carbonell, Ester Patrize Lopez Aguilar, Tulla Bee Picardi, Seth Crawford, Audrey K. Bowden, S. M. Hadi Hosseini

{"title":"Wearable fNIRS platform for dense sampling and precision functional neuroimaging","authors":"Ali Rahimpour Jounghani, Anupam Kumar, Laura Moreno Carbonell, Ester Patrize Lopez Aguilar, Tulla Bee Picardi, Seth Crawford, Audrey K. Bowden, S. M. Hadi Hosseini","doi":"10.1038/s41746-025-01690-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01690-3","url":null,"abstract":"Precision mental health aims to improve care by tailoring interventions based on individual neurobiological features. Functional near-infrared spectroscopy (fNIRS) is a cost-effective and portable alternative to traditional neuroimaging, making it a promising tool for this purpose. This study evaluates a self-administered, wearable fNIRS platform designed for precision mental health applications, focusing on its reliability and specificity in capturing individualized functional connectivity patterns. The platform incorporates a wireless, portable multichannel fNIRS device, augmented reality guidance for reproducible device placement, and a cloud-based system for remote data access. In this proof-of-concept study, eight adults completed ten dense-sampled sessions involving cognitive tasks and resting-state measurements. Results demonstrated high test-retest reliability and within-participant consistency in functional connectivity and activation patterns. These findings support the platform’s feasibility for individualized functional mapping. Future research with larger and more diverse cohorts, including clinical populations, is necessary to explore its potential for disorder-specific applications.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"3 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143940205","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

Multimodal generative AI for interpreting 3D medical images and videos. 用于解释3D医学图像和视频的多模态生成AI。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-05-13 DOI: 10.1038/s41746-025-01649-4

Jung-Oh Lee,Hong-Yu Zhou,Tyler M Berzin,Daniel K Sodickson,Pranav Rajpurkar

引用次数: 0