NPJ Digital Medicine最新文献_第8页

SQL on FHIR - Tabular views of FHIR data using FHIRPath 关于FHIR的SQL -使用FHIRPath的FHIR数据的表格视图

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-09 DOI: 10.1038/s41746-025-01708-w

John Grimes, Ryan Brush, Nikolai Rhyzhikov, Piotr Szul, Joshua Mandel, Dan Gottlieb, Grahame Grieve, Bashir Sadjad, Arjun Sanyal

引用次数: 0

Evaluating evidence-based health information from generative AI using a cross-sectional study with laypeople seeking screening information 通过横断面研究评估生成式人工智能的循证健康信息，外行人寻求筛查信息

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-09 DOI: 10.1038/s41746-025-01752-6

Felix G. Rebitschek, Alessandra Carella, Silja Kohlrausch-Pazin, Michael Zitzmann, Anke Steckelberg, Christoph Wilhelm

{"title":"Evaluating evidence-based health information from generative AI using a cross-sectional study with laypeople seeking screening information","authors":"Felix G. Rebitschek, Alessandra Carella, Silja Kohlrausch-Pazin, Michael Zitzmann, Anke Steckelberg, Christoph Wilhelm","doi":"10.1038/s41746-025-01752-6","DOIUrl":"https://doi.org/10.1038/s41746-025-01752-6","url":null,"abstract":"Large language models (LLMs) are used to seek health information. Guidelines for evidence-based health communication require the presentation of the best available evidence to support informed decision-making. We investigate the prompt-dependent guideline compliance of LLMs and evaluate a minimal behavioural intervention for boosting laypeople’s prompting. Study 1 systematically varied prompt informedness, topic, and LLMs to evaluate compliance. Study 2 randomized 300 participants to three LLMs under standard or boosted prompting conditions. Blinded raters assessed LLM response with two instruments. Study 1 found that LLMs failed evidence-based health communication standards. The quality of responses was found to be contingent upon prompt informedness. Study 2 revealed that laypeople frequently generated poor-quality responses. The simple boost improved response quality, though it remained below required standards. These findings underscore the inadequacy of LLMs as a standalone health communication tool. Integrating LLMs with evidence-based frameworks, enhancing their reasoning and interfaces, and teaching prompting are essential. Study Registration: German Clinical Trials Register (DRKS) (Reg. No.: DRKS00035228, registered on 15 October 2024).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"3 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144252764","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

Foundation versus domain-specific models for left ventricular segmentation on cardiac ultrasound 心脏超声左心室分割的基础与领域特异性模型

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-06 DOI: 10.1038/s41746-025-01730-y

Chieh-Ju Chao, Yunqi Richard Gu, Wasan Kumar, Tiange Xiang, Lalith Appari, Justin Wu, Juan M. Farina, Rachael Wraith, Jiwoon Jeong, Reza Arsanjani, Garvan C. Kane, Jae K. Oh, Curtis P. Langlotz, Imon Banerjee, Li Fei-Fei, Ehsan Adeli

{"title":"Foundation versus domain-specific models for left ventricular segmentation on cardiac ultrasound","authors":"Chieh-Ju Chao, Yunqi Richard Gu, Wasan Kumar, Tiange Xiang, Lalith Appari, Justin Wu, Juan M. Farina, Rachael Wraith, Jiwoon Jeong, Reza Arsanjani, Garvan C. Kane, Jae K. Oh, Curtis P. Langlotz, Imon Banerjee, Li Fei-Fei, Ehsan Adeli","doi":"10.1038/s41746-025-01730-y","DOIUrl":"https://doi.org/10.1038/s41746-025-01730-y","url":null,"abstract":"The Segment Anything Model (SAM) was fine-tuned on the EchoNet-Dynamic dataset and evaluated on external transthoracic echocardiography (TTE) and Point-of-Care Ultrasound (POCUS) datasets from CAMUS (University Hospital of St Etienne) and Mayo Clinic (99 patients: 58 TTE, 41 POCUS). Fine-tuned SAM was superior or comparable to MedSAM. The fine-tuned SAM also outperformed EchoNet and U-Net models, demonstrating strong generalization, especially on apical 2-chamber (A2C) images (fine-tuned SAM vs. EchoNet: CAMUS-A2C: DSC 0.891 ± 0.040 vs. 0.752 ± 0.196, p < 0.0001) and POCUS (DSC 0.857 ± 0.047 vs. 0.667 ± 0.279, p < 0.0001). Additionally, SAM-enhanced workflow reduced annotation time by 50% (11.6 ± 4.5 sec vs. 5.7 ± 1.7 sec, p < 0.0001) while maintaining segmentation quality. We demonstrated an effective strategy for fine-tuning a vision foundation model for enhancing clinical workflow efficiency and supporting human-AI collaboration.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"14 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236929","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

Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data 使用临床记录数据的机器学习预测急性缺血性卒中的半影核心不匹配

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-06 DOI: 10.1038/s41746-025-01703-1

Shaun Kohli, Parul Agarwal, “Andy” Ho Wing Chan, Asala Erekat, Girish Nadkarni, Benjamin Kummer

{"title":"Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data","authors":"Shaun Kohli, Parul Agarwal, “Andy” Ho Wing Chan, Asala Erekat, Girish Nadkarni, Benjamin Kummer","doi":"10.1038/s41746-025-01703-1","DOIUrl":"https://doi.org/10.1038/s41746-025-01703-1","url":null,"abstract":"In acute ischemic stroke due to large-vessel occlusion (AIS-LVO), late-window endovascular thrombectomy (EVT) decisions depend on penumbra-to-core (P:C) mismatch from computed tomographic perfusion (CTP). We developed multiple machine learning (ML) models to predict P:C ratios from a retrospectively-identified cohort of AIS-LVO patients who underwent CTP within 30 min of initial neuroimaging, using non-imaging electronic health record (EHR) data available prior to CTP evaluation. We extracted structured data and free-text clinical notes from the EHR, generating document embeddings as sums of BioWordVec vectors weighted by term-frequency-inverse-document-frequency scores. We identified 120 patients; an extreme-gradient-boosting model classified P:C ratios as ≥ or <1.8, achieving an AUROC of 0.80 (95% CI 0.57–0.92) with optimal performance using text limited to 500 characters. Sensitivity was 0.80, specificity 0.66, and F1 score 0.86. Our findings suggest that ML models leveraging real-world non-imaging data can potentially aid LVO-AIS triage, though further validation is needed.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"55 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144228743","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

Author Correction: Small language models learn enhanced reasoning skills from medical textbooks. 作者更正：小语言模型从医学教科书中学习增强的推理技能。

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2025-06-06 DOI: 10.1038/s41746-025-01745-5

Hyunjae Kim, Hyeon Hwang, Jiwoo Lee, Sihyeon Park, Dain Kim, Taewhoo Lee, Chanwoong Yoon, Jiwoong Sohn, Jungwoo Park, Olga Reykhart, Thomas Fetherston, Donghee Choi, Soo Heon Kwak, Qingyu Chen, Jaewoo Kang

引用次数: 0

Guiding responsible AI in healthcare in the Philippines 指导菲律宾医疗保健领域负责任的人工智能

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-05 DOI: 10.1038/s41746-025-01755-3

Raymond Francis R. Sarmiento, Shauna M. Overgaard, Chenyu Gai, Joshua D. Overgaard, Joshua W. Ohde

引用次数: 0

Randomized clinical trial of a digital medication system to enhance adherence in patients with severe mental disorders 提高严重精神障碍患者依从性的数字药物系统的随机临床试验

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-05 DOI: 10.1038/s41746-025-01748-2

Jia Zhou, Qian Zhai, Han Qi, Xiaolei Jin, Cunli Xiao, Wenxiu Li, Junwei Song, Lei Feng, Haibo Wang, Chengcheng Dong, Zibo Yu, Yuan Feng, Gang Wang, Fang Yan

{"title":"Randomized clinical trial of a digital medication system to enhance adherence in patients with severe mental disorders","authors":"Jia Zhou, Qian Zhai, Han Qi, Xiaolei Jin, Cunli Xiao, Wenxiu Li, Junwei Song, Lei Feng, Haibo Wang, Chengcheng Dong, Zibo Yu, Yuan Feng, Gang Wang, Fang Yan","doi":"10.1038/s41746-025-01748-2","DOIUrl":"https://doi.org/10.1038/s41746-025-01748-2","url":null,"abstract":"To evaluate the effectiveness of a digital medication system in improving adherence among patients with serious mental disorders (SMD), we conducted a cluster-randomized controlled trial across 30 communities in Beijing. Participants, aged 18–65 years, were diagnosed with schizophrenia or bipolar disorder and either received intermittent medication or refused treatment. Recruitment occurred from September 2, 2022, to January 12, 2023. The intervention group received a digital medication system. The control group used an online medication diary. The primary outcome was poor adherence, defined as missing 20% or more of prescribed doses at 12 months. Among 216 recruited patients, 206 completed the study. The intervention group showed significantly higher adherence (84/108 vs. 23/108), with an adjusted risk difference of 52.34% (95% CI: 34.65%–70.03%; P < 0.0001). This trial provides the first robust evidence that the digital medication system can significantly improve medication adherence in patients with SMD. Trial Registration: The trial was registered on the Chinese Clinical Trial Registry (chictr.org.cn) on May 29, 2022 (ChiCTR-ICR-2200060359).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"43 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144218733","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 “True Lifecycle Approach” towards governing healthcare AI with the GCC as a global governance model 以GCC为全球治理模式，采用“真正的生命周期方法”治理医疗保健人工智能

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-05 DOI: 10.1038/s41746-025-01614-1

Barry Solaiman, Yosra Magdi Mekki, Junaid Qadir, Mohammed Ghaly, Mohamed Abdelkareem, Abdulla Al-Ansari

引用次数: 0

When investigator meets large language models: a qualitative analysis of cancer patient decision-making journeys 当研究者遇到大型语言模型：癌症患者决策过程的定性分析

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-05 DOI: 10.1038/s41746-025-01747-3

Neta Shanwetter Levit, Mor Saban

{"title":"When investigator meets large language models: a qualitative analysis of cancer patient decision-making journeys","authors":"Neta Shanwetter Levit, Mor Saban","doi":"10.1038/s41746-025-01747-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01747-3","url":null,"abstract":"Large language models (LLMs) are transforming the landscape of healthcare research, yet their role in qualitative analysis remains underexplored. This study compares human-led and LLM-assisted approaches to analyzing cancer patient narratives, using 33 semi-structured interviews. We conducted three parallel analyses: investigator-led thematic analysis, ChatGPT-4o, and Gemini Advance Pro 1.5. The investigator-led approach identified psychosocial and emotional themes, while the LLMs highlighted structural, temporal, and logistical aspects. LLMs demonstrated efficiency in identifying recurring patterns but struggled with emotional nuance and contextual depth. Investigator-led analysis, while time-intensive, captured the complexities of identity disruption and emotional processing. Our findings suggest that LLMs can serve as complementary tools in qualitative research, enhancing analytical breadth when paired with human interpretation. This study proposes a hybrid model integrating AI-assisted and human-led methods and offers practical recommendations for responsibly incorporating LLMs into qualitative health research.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"19 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144228744","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

Identifying and mitigating algorithmic bias in the safety net 识别和减轻安全网络中的算法偏见

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-06-05 DOI: 10.1038/s41746-025-01732-w

Shaina Mackin, Vincent J. Major, Rumi Chunara, Remle Newton-Dame

{"title":"Identifying and mitigating algorithmic bias in the safety net","authors":"Shaina Mackin, Vincent J. Major, Rumi Chunara, Remle Newton-Dame","doi":"10.1038/s41746-025-01732-w","DOIUrl":"https://doi.org/10.1038/s41746-025-01732-w","url":null,"abstract":"Algorithmic bias occurs when predictive model performance varies meaningfully across sociodemographic classes, exacerbating systemic healthcare disparities. NYC Health + Hospitals, an urban safety net system, assessed bias in two binary classification models in our electronic medical record: one predicting acute visits for asthma and one predicting unplanned readmissions. We evaluated differences in subgroup performance across race/ethnicity, sex, language, and insurance using equal opportunity difference (EOD), a metric comparing false negative rates. The most biased classes (race/ethnicity for asthma, insurance for readmission) were targeted for mitigation using threshold adjustment, which adjusts subgroup thresholds to minimize EOD, and reject option classification, which re-classifies scores near the threshold by subgroup. Successful mitigation was defined as 1) absolute subgroup EODs <5 percentage points, 2) accuracy reduction <10%, and 3) alert rate change <20%. Threshold adjustment met these criteria; reject option classification did not. We introduce a Supplementary Playbook outlining our approach for low-resource bias mitigation.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"16 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144218731","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