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Ecological momentary assessments for patients with hereditary angioedema: a feasibility and acceptability controlled study. 遗传性血管性水肿患者的瞬时生态评价:一项可行性和可接受性对照研究。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-12 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1693550
Monica Parati, Luca Ranucci, Azzurra Cesoni Marcelli, Lorenza Chiara Zingale, Beatrice De Maria, Clara Gino, Aida Zulueta, Riccardo Sideri, Alessandra Gorini, Francesca Perego

Introduction: Hereditary angioedema (HAE) is a rare disease imposing a significant quality of life burden. Affect monitoring via Ecological Momentary Assessment (EMA) could offer personalized psychological support by collecting repeated, ecological data in real-life, overcoming the limitations of traditional methods. This study assessed the feasibility and acceptability of an EMA protocol for affect monitoring in HAE patients vs. healthy controls (CTR).

Methods: HAE patients and CTR were recruited for a 16-week EMA study. Participants received weekly EMA surveys assessing affect via REDCap™. Feasibility was evaluated through recruitment, response, and completion rates. Acceptability was assessed via a post-study questionnaire through a visual analogue scale ranging from 1 to 100.

Results: Twenty-eight Caucasian subjects were contacted, 12 HAE [median age: 50 (22) years, 5 males] and 14 CTR [age: 30 (32) years, 6 males] agreed to participate, resulting in a recruitment rate of 93%. Response and completion rates were ≥92% and ≥96% respectively in both groups. Completion time was brief and did not differ between groups [HAE: 1' 28″ (29″) vs. CTR: 1' 15' (15″), P = 0.274]. The protocol was considered acceptable by both groups [HAE: rate 83.5 (18.8) vs. CTR: 72.0 (13.0), p = 0.27] with HAE rating the experience as helpful [79 (39.8)] and thought-provoking [67 (33)].

Conclusion: EMA is a highly feasible and acceptable method for affect monitoring in HAE. The presence of a rare disease does not appear to be a barrier to its application, supporting its use in this clinical setting.

遗传性血管性水肿(HAE)是一种严重影响生活质量的罕见疾病。通过生态瞬时评估(EMA)进行影响监测,可以通过收集现实生活中重复的生态数据,克服传统方法的局限性,提供个性化的心理支持。本研究评估了EMA方案在HAE患者与健康对照(CTR)中进行影响监测的可行性和可接受性。方法:招募HAE患者和CTR进行为期16周的EMA研究。参与者通过REDCap™每周接受EMA调查评估影响。通过招募、响应和完成率来评估可行性。可接受性通过研究后问卷通过视觉模拟量表从1到100进行评估。结果:联系了28名高加索受试者,12名HAE[中位年龄:50(22)岁,5名男性]和14名CTR[年龄:30(32)岁,6名男性]同意参与,招募率为93%。两组有效率和完成率分别为≥92%和≥96%。完成时间很短,两组间无差异[HAE: 1' 28″(29″)vs. CTR: 1' 15'(15″),P = 0.274]。两组都认为该方案是可接受的[HAE:率83.5 (18.8)vs. CTR: 72.0 (13.0), p = 0.27], HAE评价该经验有帮助[79(39.8)]和发人深省[67(33)]。结论:EMA是一种高度可行和可接受的HAE患者影响监测方法。罕见疾病的存在似乎不会成为其应用的障碍,支持其在临床环境中的使用。
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引用次数: 0
Artificial intelligence assessment of valvular disease and ventricular function by a single echocardiography view. 单次超声心动图对瓣膜疾病和心室功能的人工智能评估。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-12 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1684933
Lior Fisher, Michael Fiman, Ella Segal, Shira Lidar, Noa Rubin, Adiel Am-Shalom, Ido Cohen, Kobi Faierstein, Avishai M Tsur, Ehud Schwammenthal, Robert Klempfner, Eyal Zimlichman, Ehud Raanani, Elad Maor

Background: Valvular heart disease and heart failure are major global health burdens, yet access to comprehensive echocardiography is often limited, particularly in resource-constrained settings. Artificial intelligence (AI) may enable rapid, point-of-care cardiac assessment using simplified imaging protocols.

Objectives: To evaluate whether a deep learning model can accurately detect significant valvular and ventricular dysfunction using only a single two-dimensional apical four-chamber echocardiographic view, including images acquired by non-cardiologists with handheld ultrasound devices.

Methods: We retrospectively analyzed 120,127 echocardiographic studies from a tertiary medical center to train and validate a deep learning model for identifying moderate-or-greater mitral or tricuspid regurgitation, right ventricular dysfunction, and reduced left ventricular ejection fraction (≤40%). A prospective cohort of 209 patients underwent handheld point-of-care cardiac ultrasound performed by non-cardiologist physicians, with same-hospitalization comprehensive echocardiography as the reference standard.

Results: In retrospective testing, model areas under the curve (AUCs) were 0.883 for mitral regurgitation, 0.913 for tricuspid regurgitation, 0.940 for right ventricular dysfunction, and 0.982 for reduced ejection fraction. In the prospective cohort, AUCs were 0.72, 0.87, 0.95, and 0.97 for the same respective targets.

Conclusions: A single-view deep learning model demonstrated strong diagnostic accuracy for detecting significant valvular and ventricular dysfunction across both standard and handheld ultrasound acquisitions. This approach may facilitate rapid, scalable cardiac function screening by non-cardiologists in diverse clinical environments.

Clinical trial registration: identifier NCT05455541.

背景:瓣膜性心脏病和心力衰竭是全球主要的健康负担,但获得全面超声心动图的机会往往有限,特别是在资源有限的情况下。人工智能(AI)可以使用简化的成像协议实现快速、即时的心脏评估。目的:评估深度学习模型是否可以仅使用单个二维尖顶四室超声心动图视图准确检测重要的瓣膜和心室功能障碍,包括非心脏病专家使用手持超声设备获得的图像。方法:我们回顾性分析了来自三级医疗中心的120,127份超声心动图研究,以训练和验证深度学习模型,以识别中度或更严重的二尖瓣或三尖瓣反流、右室功能障碍和左室射血分数降低(≤40%)。前瞻性队列209例患者接受由非心脏病专家医师进行的手持式即时心脏超声检查,以同一院综合超声心动图作为参考标准。结果:回顾性分析,二尖瓣反流模型曲线下面积为0.883,三尖瓣反流模型曲线下面积为0.913,右室功能障碍模型曲线下面积为0.940,射血分数降低模型曲线下面积为0.982。在前瞻性队列中,相同目标的auc分别为0.72、0.87、0.95和0.97。结论:单视图深度学习模型在标准和手持式超声采集中检测显著的瓣膜和心室功能障碍方面表现出很强的诊断准确性。这种方法可以促进非心脏病专家在不同临床环境中的快速、可扩展的心功能筛查。临床试验注册:标识符NCT05455541。
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引用次数: 0
Combining shallow and deep neural networks on pseudo-color enhanced images for digital breast tomosynthesis lesion classification. 基于伪彩色增强图像的浅神经网络与深度神经网络相结合用于数字乳腺断层合成病变分类。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1705044
Zhikai Yang, Yingqing Liu, Örjan Smedby, Rodrigo Moreno

Introduction: The classification of lesion types in Digital Breast Tomosynthesis (DBT) images is crucial for the early diagnosis of breast cancer. However, the task remains challenging due to the complexity of breast tissue and the subtle nature of lesions. To alleviate radiologists' workload, computer-aided diagnosis (CAD) systems have been developed. The breast lesion regions vary in size and complexity, which leads to performance degradation.

Methods: To tackle this problem, we propose a novel DBT Dual-Net architecture comprising two complementary neural network branches that extract both low-level and high-level features. By fusing different-level feature representations, the model can better capture subtle structure. Furthermore, we introduced a pseudo-color enhancement procedure to improve the visibility of lesions on DBT. Moreover, most existing DBT classification studies rely on two-dimensional (2D) slice-level analysis, neglecting the rich three-dimensional (3D) spatial context within DBT volumes. To address this limitation, we used majority voting for image-level classification from predictions across slices.

Results: We evaluated our method on a public DBT dataset and compared its performance with several existing classification approaches. The results showed that our method outperforms baseline models.

Discussion: The use of pseudo-color enhancement, extracting high and low-level features and inter-slice majority voting proposed method is effective for lesion classification in DBT. The code is available at https://github.com/xiaoerlaigeid/DBT-Dual-Net.

数字化乳腺断层合成(DBT)图像中病变类型的分类对于乳腺癌的早期诊断至关重要。然而,由于乳腺组织的复杂性和病变的微妙性质,这项任务仍然具有挑战性。为了减轻放射科医生的工作量,计算机辅助诊断(CAD)系统被开发出来。乳腺病变区域的大小和复杂程度各不相同,导致性能下降。方法:为了解决这一问题,我们提出了一种新的DBT双网架构,该架构包括两个互补的神经网络分支,分别提取低级和高级特征。通过融合不同层次的特征表示,该模型可以更好地捕捉细微结构。此外,我们引入了一种伪彩色增强程序来提高DBT上病变的可见性。此外,大多数现有的DBT分类研究依赖于二维(2D)切片水平分析,忽视了DBT体积内丰富的三维(3D)空间背景。为了解决这一限制,我们使用多数投票对跨切片的预测进行图像级分类。结果:我们在公共DBT数据集上评估了我们的方法,并将其与几种现有分类方法的性能进行了比较。结果表明,我们的方法优于基线模型。讨论:采用伪彩色增强、高低特征提取和层间多数投票提出的方法对DBT病变分类是有效的。代码可在https://github.com/xiaoerlaigeid/DBT-Dual-Net上获得。
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引用次数: 0
Feasibility of one-month home-based HRV monitoring in ASD: a case study using smart clothing technology. 在ASD中进行为期一个月的家庭HRV监测的可行性:使用智能服装技术的案例研究。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1741400
Soichiro Matsuda, Yurina Shinohara

Background: Sleep disturbances and autonomic dysregulation are common in autism spectrum disorder (ASD), yet few studies have examined long-term nocturnal heart rate variability (HRV) in home settings.

Objective: This study evaluated the feasibility of one-month home-based HRV monitoring using smart clothing in a preschooler with ASD, and explored whether nocturnal HRV predicts next-day problem behaviors.

Methods: HRV was recorded nightly for 25 valid days using a garment-type wearable ECG. Problem behaviors were reported daily by caregivers. HRV indices were compared between nights preceding days with and without problem behaviors using Wilcoxon signed-rank tests.

Results: No significant differences in total sleep time or HRV indices were found between the two day types.

Conclusion: Although HRV did not predict next-day behavior, the study demonstrates the feasibility and methodological transparency of long-term home-based physiological monitoring in young children with ASD.

背景:睡眠障碍和自主神经失调在自闭症谱系障碍(ASD)中很常见,但很少有研究在家庭环境中检测长期夜间心率变异性(HRV)。目的:本研究评估使用智能服装对ASD学龄前儿童进行为期一个月的HRV家庭监测的可行性,并探讨夜间HRV是否能预测次日的问题行为。方法:采用穿戴式心电仪连续25天每晚记录心率。护理人员每天都会报告问题行为。使用Wilcoxon符号秩检验比较有和没有问题行为的前一天晚上的HRV指数。结果:两种睡眠类型在总睡眠时间和HRV指数上无显著差异。结论:虽然HRV不能预测第二天的行为,但该研究证明了长期家庭生理监测幼儿ASD的可行性和方法的透明度。
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引用次数: 0
AI-supported clinical decision-making: in silico simulation of physician-AI interactions. 人工智能支持的临床决策:医生与人工智能互动的计算机模拟。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1697825
Amun Hofmann

Objective: While the integration of modern AI systems in clinical practice is currently in the process of transforming how medicine is being practiced, the focus of most research activities lies on AI-associated efficacy and safety. However, the interplay between human agents and AI systems will equally shape the actual impact of such systems.

Methods: This study simulated human decision-making using 27 agents characterized by varying levels of competence, certainty, and trust. Agents completed binary and three-option decision tasks, both with and without AI assistance. AI models varied in competence (0.3-0.9) and, in some simulations, included confidence signals to influence human trust dynamically. Each scenario involved 10,000 simulated decisions per agent. In AI-assisted conditions, decisions were modulated by the agent's baseline trust and, in the conditional trust setting, the AI's expressed confidence.

Results: AI support significantly improved decision accuracy for most agents, especially those with high competence but low certainty. In binary tasks, agents showed up to 150% relative improvement in decision accuracy with AI competence ≥0.6. In three-option tasks, even lower-performing AI (e.g., 0.4 competence) enhanced decision results. Conditional trust simulations showed further gains, particularly among agents with moderate baseline trust, as dynamic trust adjustments based on AI confidence reduced over-reliance on poor AI recommendations.

Discussion: Results demonstrate that AI assistance, particularly when paired with confidence calibration, enhances human decision-making, especially for uncertain or moderately skilled users. However, over-trusting low-competence AI can impair outcomes for high-performing agents. Tailored AI-human collaboration strategies are essential for optimizing clinical decision support.

目的:虽然现代人工智能系统在临床实践中的整合目前正在改变医学实践的过程中,但大多数研究活动的重点在于人工智能相关的疗效和安全性。然而,人类代理和人工智能系统之间的相互作用同样会影响这些系统的实际影响。方法:本研究使用27个具有不同能力、确定性和信任水平的代理来模拟人类决策。在有和没有人工智能帮助的情况下,智能体完成了二元和三选项决策任务。人工智能模型的能力各不相同(0.3-0.9),在一些模拟中,包括动态影响人类信任的信心信号。每个场景涉及每个代理10,000个模拟决策。在人工智能辅助条件下,决策由代理的基线信任调节,在条件信任设置下,人工智能表达了信心。结果:人工智能支持显著提高了大多数代理的决策准确性,特别是那些高能力但低确定性的代理。在二元任务中,当人工智能能力≥0.6时,智能体的决策准确率相对提高150%。在三选项任务中,即使表现较差的AI(例如,0.4能力)也能增强决策结果。条件信任模拟显示了进一步的收益,特别是在具有中等基线信任的代理中,因为基于人工智能信心的动态信任调整减少了对糟糕的人工智能建议的过度依赖。讨论:结果表明,人工智能辅助,特别是与置信度校准配对时,可以增强人类的决策,特别是对于不确定或中等技能的用户。然而,过度信任低能力的人工智能会损害高绩效代理的结果。量身定制的人工智能-人类协作策略对于优化临床决策支持至关重要。
{"title":"AI-supported clinical decision-making: in silico simulation of physician-AI interactions.","authors":"Amun Hofmann","doi":"10.3389/fdgth.2025.1697825","DOIUrl":"10.3389/fdgth.2025.1697825","url":null,"abstract":"<p><strong>Objective: </strong>While the integration of modern AI systems in clinical practice is currently in the process of transforming how medicine is being practiced, the focus of most research activities lies on AI-associated efficacy and safety. However, the interplay between human agents and AI systems will equally shape the actual impact of such systems.</p><p><strong>Methods: </strong>This study simulated human decision-making using 27 agents characterized by varying levels of competence, certainty, and trust. Agents completed binary and three-option decision tasks, both with and without AI assistance. AI models varied in competence (0.3-0.9) and, in some simulations, included confidence signals to influence human trust dynamically. Each scenario involved 10,000 simulated decisions per agent. In AI-assisted conditions, decisions were modulated by the agent's baseline trust and, in the conditional trust setting, the AI's expressed confidence.</p><p><strong>Results: </strong>AI support significantly improved decision accuracy for most agents, especially those with high competence but low certainty. In binary tasks, agents showed up to 150% relative improvement in decision accuracy with AI competence ≥0.6. In three-option tasks, even lower-performing AI (e.g., 0.4 competence) enhanced decision results. Conditional trust simulations showed further gains, particularly among agents with moderate baseline trust, as dynamic trust adjustments based on AI confidence reduced over-reliance on poor AI recommendations.</p><p><strong>Discussion: </strong>Results demonstrate that AI assistance, particularly when paired with confidence calibration, enhances human decision-making, especially for uncertain or moderately skilled users. However, over-trusting low-competence AI can impair outcomes for high-performing agents. Tailored AI-human collaboration strategies are essential for optimizing clinical decision support.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1697825"},"PeriodicalIF":3.2,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12827601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146054938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Artificial intelligence, extended reality, and emerging AI-XR integrations in medical education. 医学教育中的人工智能、扩展现实和新兴的AI-XR集成。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1740557
Talia Tene, Diego Fabián Vique López, Marlene Jacqueline García Veloz, Byron Stalin Rojas Oviedo, Richard Tene-Fernandez

Introduction: Artificial intelligence (AI) and extended reality (XR)-including virtual, augmented, and mixed reality-are increasingly adopted in health-professions education. However, the educational impact of AI, XR, and especially their combined use within integrated AI-XR ecosystems remains incompletely characterized.

Objective: To synthesize empirical evidence on educational outcomes and implementation considerations for AI-, XR-, and combined AI-XR-based interventions in medical and health-professions education.

Methods: Following PRISMA and PICO guidance, we searched three databases (Scopus, PubMed, IEEE Xplore) and screened records using predefined eligibility criteria targeting empirical evaluations in health-professions education. After deduplication (336 records removed) and two-stage screening, 13 studies published between 2019 and 2024 were included. Data were extracted on learner population, clinical domain, AI/XR modality, comparators, outcomes, and implementation factors, and narratively synthesized due to heterogeneity in designs and measures.

Results: The 13 included studies involved undergraduate and postgraduate learners in areas such as procedural training, clinical decision-making, and communication skills. Only a minority explicitly integrated AI with XR within the same intervention; most evaluated AI-based or XR-based approaches in isolation. Across this mixed body of work, studies more often than not reported gains in at least one outcome-knowledge or skills performance, task accuracy, procedural time, or learner engagement-relative to conventional instruction, alongside generally high acceptability. Recurrent constraints included costs, technical reliability, usability, faculty readiness, digital literacy, and data privacy and ethics concerns.

Conclusions: Current evidence on AI, XR, and emerging AI-XR integrations suggests promising but preliminary benefits for learning and performance. The small number of fully integrated AI-XR interventions and the methodological limitations of many primary studies substantially limit the certainty and generalizability of these findings. Future research should use more rigorous and standardized designs, explicitly compare AI-only, XR-only, and AI-XR hybrid approaches, and be coupled with faculty development, robust technical support, and alignment with competency-based assessment.

人工智能(AI)和扩展现实(XR)——包括虚拟、增强和混合现实——越来越多地应用于卫生专业教育。然而,人工智能、XR的教育影响,特别是它们在集成的人工智能-XR生态系统中的结合使用,仍然没有完全表征。目的:综合医疗卫生专业教育中基于人工智能、XR和人工智能-XR联合干预措施的教育效果和实施考虑因素的经验证据。方法:在PRISMA和PICO的指导下,我们检索了三个数据库(Scopus、PubMed、IEEE Xplore),并使用针对卫生专业教育经验评估的预定义资格标准筛选记录。经过重复数据删除(删除336条记录)和两阶段筛选,纳入了2019年至2024年间发表的13项研究。从学习者群体、临床领域、人工智能/XR模式、比较物、结果和实施因素等方面提取数据,并由于设计和测量的异质性而进行叙述性综合。结果:这13项研究涉及本科生和研究生,涉及程序训练、临床决策和沟通技巧等领域。只有少数人在同一干预中明确将AI与XR结合起来;大多数评估是孤立的基于人工智能或基于x射线的方法。在这种混合的工作中,研究往往报告了至少在一个方面的成果——知识或技能表现、任务准确性、程序时间或学习者参与——相对于传统教学而言,以及普遍较高的可接受性。经常出现的限制包括成本、技术可靠性、可用性、教师准备、数字素养以及数据隐私和道德问题。结论:目前关于人工智能、XR和新兴AI-XR集成的证据表明,在学习和绩效方面有希望但初步的好处。充分整合AI-XR干预措施的数量较少,以及许多初级研究方法上的局限性,极大地限制了这些发现的确定性和可推广性。未来的研究应该使用更严格和标准化的设计,明确比较纯人工智能、纯xr和人工智能- xr混合方法,并与教师发展、强大的技术支持和基于能力的评估相结合。
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引用次数: 0
Extracting structured data from unstructured breast imaging reports with transformer-based models. 利用基于变压器的模型从非结构化乳房成像报告中提取结构化数据。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1718330
Mikel Carrilero-Mardones, Jorge Pérez-Martín, Francisco Javier Díez, Iñigo Bermejo Delgado

Background and objective: Structured clinical data is essential for research and informed decision-making, yet medical reports are frequently stored as unstructured free text. This study compared the performance of BERT-based and generative language models in converting unstructured breast imaging reports into structured, tabular data suitable for clinical and research applications.

Methods: A dataset of 286 anonymised breast imaging reports in Spanish was translated into English and used to evaluate five transformer-based models pre-trained in medical data: BlueBERT, BioBERT, BioMedBERT, BioGPT and ClinicalT5. Two natural language processing approaches were explored: classification of 19 categorical variables (e.g. diagnostic technique, report type, family history, BI-RADS category, tumour shape and margin) and extractive question answering of four entities (patient age, patient history, parenchymal distortion or asymmetries, and tumour size). Multiple fine-tuning strategies and input configurations were tested for each model, and performance was evaluated using accuracy and macro F1 scores.

Results: BioGPT demonstrated the best performance in classification tasks, achieving an overall accuracy of 96.10 % and a macro F1 score of 90.30 % . This was significantly better than BERT-based models ( p = 0.012 for accuracy and p = 0.017 for F1), particularly in underrepresented categories such as tumour descriptors. In extractive question answering tasks, BioGPT achieved an average accuracy of 93.24 % , which is slightly lower than that of BioMedBERT and ClinicalT5, but not significantly so. Notably, BioGPT could perform classification and extractive question answering simultaneously, which is a capability unavailable in BERT-like models.

Conclusions: Generative models, particularly BioGPT, offer a robust and scalable approach to automating the extraction of structured information from unstructured breast imaging reports. Their superior performance, combined with their ability to handle multiple tasks concurrently, highlights their potential to reduce the manual effort required for clinical data curation and to enable the efficient integration of imaging data into research and clinical workflows.

背景和目的:结构化临床数据对于研究和知情决策至关重要,但医疗报告通常以非结构化自由文本的形式存储。本研究比较了基于bert和生成语言模型在将非结构化乳房成像报告转换为适合临床和研究应用的结构化表格数据方面的性能。方法:将286份西班牙语匿名乳房成像报告数据集翻译成英文,并用于评估五种基于医疗数据预训练的转换器模型:BlueBERT、BioBERT、BioMedBERT、BioGPT和ClinicalT5。我们探索了两种自然语言处理方法:对19个分类变量(如诊断技术、报告类型、家族史、BI-RADS类别、肿瘤形状和边缘)进行分类,并对4个实体(患者年龄、病史、实质扭曲或不对称、肿瘤大小)进行抽取问题回答。针对每个模型测试了多种微调策略和输入配置,并使用准确性和宏观F1分数来评估性能。结果:BioGPT在分类任务中表现最佳,总体准确率为96.10%,宏观F1得分为90.30%。这明显优于基于bert的模型(准确性p = 0.012, F1 p = 0.017),特别是在代表性不足的类别中,如肿瘤描述符。在抽取性问答任务中,BioGPT的平均准确率为93.24%,略低于BioMedBERT和ClinicalT5,但差异不显著。值得注意的是,BioGPT可以同时执行分类和抽取问题回答,这是bert类模型所不具备的能力。结论:生成模型,特别是BioGPT,提供了一种强大的、可扩展的方法,可以从非结构化的乳房成像报告中自动提取结构化信息。它们卓越的性能,加上同时处理多项任务的能力,突显了它们在减少临床数据管理所需的人工工作方面的潜力,并使成像数据能够有效地集成到研究和临床工作流程中。
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引用次数: 0
Enhancing healthcare outcome with scalable processing and predictive analytics via cloud healthcare API. 通过云医疗保健API使用可扩展处理和预测分析增强医疗保健结果。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1687131
Seyede Sana Salehi, Hamid Saadatfar, Solomon Sunday Oyelere, Sadiq Hussain, Javad Hassannataj Joloudari, Mohammad Taheri Ledari, Emrah Arslan, Behnam Barzegar

This systematic literature review investigates the Google Cloud Healthcare API's role in transforming healthcare delivery through advanced analytics, machine learning, and cloud-based solutions. The study examines current features of cloud-based healthcare platforms in managing heterogeneous healthcare data formats, analyzes the effectiveness of cloud solutions in enhancing clinical outcomes, and compares Google Cloud Healthcare API with alternative platforms. The findings reveal that Google Cloud Healthcare API demonstrates notable advantages through its fully managed, serverless architecture, native support for healthcare standards (e.g., FHIR, HL7v2, DICOM), and seamless integration with advanced AI/ML services. Cloud-based predictive analytics platforms have proven effective in reducing hospital readmissions, addressing physician burnout, and enabling scalable telemedicine solutions. However, significant challenges persist including data privacy concerns, regulatory compliance complexities, infrastructure dependencies, and potential vendor lock-in risks. The research demonstrates that healthcare organizations implementing comprehensive cloud-based solutions achieve measurable improvements in patient outcomes, operational efficiency, and care delivery models. While technical challenges around latency in medical imaging and interoperability remain, the evidence strongly supports cloud adoption for healthcare transformation, provided organizations address security, compliance, and implementation challenges through strategic planning and comprehensive change management approaches.

这篇系统的文献综述调查了谷歌云医疗API在通过高级分析、机器学习和基于云的解决方案转变医疗保健服务中的作用。该研究考察了基于云的医疗保健平台在管理异构医疗保健数据格式方面的当前特征,分析了云解决方案在增强临床结果方面的有效性,并将谷歌云医疗保健API与替代平台进行了比较。调查结果显示,谷歌云医疗保健API通过其完全托管的无服务器架构、对医疗保健标准(例如,FHIR、HL7v2、DICOM)的本地支持以及与高级AI/ML服务的无缝集成,展示了显著的优势。事实证明,基于云的预测分析平台在减少医院再入院、解决医生职业倦怠和实现可扩展的远程医疗解决方案方面是有效的。然而,重大挑战仍然存在,包括数据隐私问题、法规遵从性复杂性、基础设施依赖性和潜在的供应商锁定风险。研究表明,实施全面的基于云的解决方案的医疗保健组织在患者结果、运营效率和护理交付模式方面取得了可衡量的改进。虽然医疗成像和互操作性方面的技术挑战仍然存在,但有证据强烈支持在医疗保健转型中采用云,前提是组织可以通过战略规划和全面的变更管理方法解决安全性、合规性和实施方面的挑战。
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引用次数: 0
Telehealth as a catalyst for smart rural development and sustainable tourism: a feasibility case study from Agrafa, Greece. 远程保健作为智慧农村发展和可持续旅游的催化剂:来自希腊阿格拉法的可行性案例研究。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1739417
Yiannis Koumpouros, Androniki Kavoura

Introduction: Rural regions often face persistent healthcare access challenges due to geographic isolation, aging populations, limited infrastructure, and seasonal fluctuations in demand. These challenges not only impact resident well-being but also hinder tourism development. While research has addressed rural healthcare or development separately, limited attention has been given to the synergies between telehealth, regional revitalization, and tourism. This study investigates how telehealth can act as a catalyst for both rural development and sustainable tourism in remote settings.

Methods: This pilot study introduces a telehealth framework using a portable diagnostic device integrated into a broader smart village strategy. The initiative was led by the Digital Innovation in Public Health Research Lab at the University of West Attica, in partnership with local authorities and private healthcare providers. Key components included: (a) cross-sector collaboration; (b) a custom-built web platform for monitoring effectiveness; (c) training of local personnel to assist with guided remote consultations; (d) use cases such as chronic disease monitoring, acute symptom triage for tourists, and digital nomad services; and (e) policy alignment at national and European levels.

Results: Preliminary qualitative findings suggest improved healthcare accessibility for residents with chronic conditions and enhanced medical support for visitors and digital nomads. The system demonstrated feasibility even in low-connectivity environments and received positive feedback from community stakeholders.

Discussion: This study contributes both theoretically and practically by advancing literature on the intersection of telehealth and rural tourism development. The framework offers a replicable model for other European rural regions seeking to enhance health equity, promote digital inclusion, and attract long-term visitors. Despite challenges-such as digital literacy, infrastructure limitations, and sustainability-the pilot illustrates the strategic potential of telehealth in underserved areas. Future research will focus on longitudinal outcomes and the policy tools needed for broader scalability.

导语:由于地理隔离、人口老龄化、基础设施有限以及需求的季节性波动,农村地区经常面临持续的医疗保健获取挑战。这些挑战不仅影响居民福祉,也阻碍旅游业的发展。虽然研究分别解决了农村保健或发展问题,但对远程保健、区域振兴和旅游业之间的协同作用的关注有限。本研究探讨了远程医疗如何成为偏远地区农村发展和可持续旅游的催化剂。方法:本试点研究引入了一种远程医疗框架,将便携式诊断设备集成到更广泛的智能村庄战略中。该倡议由西阿提卡大学公共卫生研究实验室的数字创新领导,与地方当局和私人医疗保健提供者合作。主要组成部分包括:(a)跨部门合作;(b)为监测有效性而定制的网络平台;(c)培训当地人员协助进行有指导的远程咨询;(d)慢性病监测、游客急性症状分类和数字游牧民服务等用例;(e)国家和欧洲层面的政策协调。结果:初步定性研究结果表明,慢性病患者的医疗可及性得到改善,游客和数字游牧民的医疗支持得到加强。即使在低连接环境中,该系统也证明了可行性,并得到了社区利益相关者的积极反馈。讨论:本研究通过推进远程医疗与乡村旅游发展交叉的文献,在理论和实践上都有贡献。该框架为寻求加强卫生公平、促进数字包容和吸引长期访客的其他欧洲农村地区提供了可复制的模式。尽管存在数字素养、基础设施限制和可持续性等挑战,但该试点表明,在服务不足的地区,远程医疗具有战略潜力。未来的研究将侧重于纵向结果和扩大可扩展性所需的政策工具。
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引用次数: 0
Wearables and behavioral coding show promise for measuring and predicting severe emotional outbursts in children. 可穿戴设备和行为编码有望测量和预测儿童的严重情绪爆发。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1641845
Guido Mascia, Hannah E Frering, Robert R Althoff, Erieshell Coney, Diana Hume Rivera, Za'Kiya Toomer-Sanders, Christine Erdie-Lalena, Mary Dame, Laura Beth Brown, Deborah Evans, Ryan S McGinnis, Ellen W McGinnis

Introduction: Temper tantrums are common in early childhood. Severe emotional outbursts, however, are transdiagnostic, disruptive, and difficult to measure across settings, highlighting the need for better methods to identify and predict these components of emotion dysregulation. To address major methodological gaps, we propose a multimodal approach combining a retrospective electronic health record (EHR) analysis (Study 1) and a pilot wearable feasibility study (Study 2) to explore new ways of predicting and quantifying emotional outbursts in children enrolled in a therapeutic day program (TDP).

Methods: In Study 1, we explored retrospective data collected from the EHR (historical patient data and hourly behavioral observations), trying to understand which variables might predict an outburst. In Study 2, wearable technology was employed to characterize outbursts leveraging free-living data collected during a typical day at a TDP. Moreover, we used these data to assess the future of possible outburst predictions among a clinical sample by analyzing the feasibility of such a technology.

Results: An EHR analysis of 45 patients aged 4-8 years revealed that observed rough behaviors at the beginning of the day were associated with an increased likelihood of subsequent outbursts (p < .001), from 6% for those with zero rough behaviors to 68% for those with two or more such behaviors. Wearable sensor data demonstrated high tolerability (all four children assented each of 3-5 days of participation for 5 h of wear) and minimal data loss (<4%). Case studies of wearable-derived heart rate, heart rate variability, and skin temperature suggested that these factors might serve as promising indicators for detecting distress and outbursts.

Discussion: Our results suggest that behavioral observation has the potential of predicting outbursts, and that wearable sensors are tolerable and feasible for children to wear. Overall, multiple methodologies should be studied concurrently and may be required to predict outbursts in the future.

在幼儿时期,发脾气是很常见的。然而,严重的情绪爆发是跨诊断的、破坏性的,并且难以跨环境测量,这突出表明需要更好的方法来识别和预测情绪失调的这些组成部分。为了解决主要的方法差距,我们提出了一种多模式方法,结合回顾性电子健康记录(EHR)分析(研究1)和可穿戴试验可行性研究(研究2),探索预测和量化参加治疗日计划(TDP)的儿童情绪爆发的新方法。方法:在研究1中,我们研究了从电子病历中收集的回顾性数据(患者历史数据和每小时行为观察),试图了解哪些变量可能预测爆发。在研究2中,采用可穿戴技术来描述在TDP典型的一天中收集的自由生活数据的爆发特征。此外,我们通过分析这种技术的可行性,使用这些数据来评估临床样本中可能的爆发预测的未来。结果:一项对45名4-8岁患者的电子病历分析显示,在一天开始时观察到的粗暴行为与随后爆发的可能性增加有关(p讨论:我们的结果表明,行为观察具有预测爆发的潜力,可穿戴传感器对于儿童来说是可容忍的和可行的。总的来说,应该同时研究多种方法,并可能需要预测未来的爆发。
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引用次数: 0
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Frontiers in digital health
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