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Correction: Effectiveness of a Web-Based Medication Education Course on Pregnant Women's Medication Information Literacy and Decision Self-Efficacy: Randomized Controlled Trial. 更正:网络用药教育课程对孕妇用药信息素养和决策自我效能的影响:随机对照试验。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-26 DOI: 10.2196/91835
Suya Li, Hui-Jun Chen, Jie Zhou, Yi-Bei Zhouchen, Rong Wang, Jinyi Guo, Sharon R Redding, Yan-Qiong Ouyang

[This corrects the article DOI: .].

[更正文章DOI: .]。
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引用次数: 0
Patient Benefits in the Context of Sepsis-Related AI-Based Clinical Decision Support Systems: Scoping Review. 脓毒症相关人工智能临床决策支持系统的患者获益:范围审查
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-26 DOI: 10.2196/76772
Pascal Raszke, Godwin Denk Giebel, Jürgen Wasem, Michael Adamzik, Hartmuth Nowak, Lars Palmowski, Philipp Heinz, Nina Timmesfeld, Marianne Tokic, Frank Martin Brunkhorst, Nikola Blase
<p><strong>Background: </strong>Global digitalization continues to advance, extending its influence into medicine and health care systems worldwide. In recent years, substantial advancements have been made in the research and development of artificial intelligence (AI), raising questions about its potential in medicine. The integration and application of AI in intensive care medicine, particularly in sepsis treatment, presents significant potential for advancing patient outcomes and enhancing patient-relevant benefits. However, a comprehensive and systematic overview of the full spectrum of patient-relevant benefits associated with AI-based clinical decision support systems (CDSS) remains lacking.</p><p><strong>Objective: </strong>This scoping review aimed to identify and categorize evidence on patient-relevant benefits of AI-based CDSS in sepsis care.</p><p><strong>Methods: </strong>Systematic research was conducted in 4 electronic databases: MEDLINE via PubMed, Embase, the ACM Digital Library, and IEEE Xplore. In addition, a comprehensive search on the websites of relevant international organizations, along with a citation search of the included articles, was conducted. Articles were included if they (1) focused on sepsis and (2) described patient-relevant benefits of AI-based CDSS. Articles published between January 1, 2008, and March 2, 2023, were considered for inclusion. Study selection was performed independently by 2 reviewers. The manuscript was drafted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. The analysis of the included articles was conducted using the program MAXQDA (VERBI Software GmbH), with systemization finalized in a consensus workshop.</p><p><strong>Results: </strong>A total of 3368 records were identified across the 4 databases, of which 24 met the inclusion criteria and were included in the scoping review. The additional search on international websites and in reference lists identified 6 more relevant articles, resulting in 30 included studies. Of these, 20 were quantitative, comprising 7 prospective and 13 retrospective designs. In addition, 1 qualitative study, 1 mixed methods study, 6 review articles, and 2 articles from institutional websites were included. Patient-relevant benefits were systematized in six main categories: (1) prediction, (2) earlier treatment and prioritization of high-risk patients, (3) individualized therapy, (4) improved patient outcomes (including improved Sequential Organ Failure Assessment score, reduced length of stay, and reduced mortality), (5) general improvements in care, and (6) reduced readmission rate.</p><p><strong>Conclusions: </strong>This scoping review underscores the potential of AI-based CDSS to positively impact patient-relevant benefits, particularly in sepsis care, where they demonstrate considerable promise for improving intensive care. However, the majority of the identified s
背景:全球数字化持续推进,并将其影响扩展到世界各地的医学和卫生保健系统。近年来,人工智能(AI)的研究和开发取得了实质性进展,人们对其在医学上的潜力提出了质疑。人工智能在重症监护医学中的整合和应用,特别是在败血症治疗中,对改善患者预后和增强患者相关益处具有重大潜力。然而,基于人工智能的临床决策支持系统(CDSS)对患者相关益处的全面和系统概述仍然缺乏。目的:本综述旨在识别和分类基于人工智能的CDSS在脓毒症治疗中患者相关益处的证据。方法:通过PubMed、Embase、ACM数字图书馆和IEEE explore 4个电子数据库进行系统研究。此外,还在有关国际组织的网站上进行了全面检索,并对所收录的文章进行了引文检索。如果文章(1)关注脓毒症,(2)描述基于人工智能的CDSS的患者相关益处,则纳入其中。在2008年1月1日至2023年3月2日期间发表的文章被纳入考虑范围。研究选择由2位评论者独立完成。手稿按照PRISMA-ScR(系统评价和荟萃分析扩展范围评价的首选报告项目)清单起草。使用MAXQDA (VERBI Software GmbH)程序对纳入的文章进行分析,并在共识研讨会上完成系统化。结果:在4个数据库中共识别出3368条记录,其中24条符合纳入标准,被纳入范围评价。在国际网站和参考文献列表上的进一步搜索确定了6篇相关文章,结果纳入了30项研究。其中20项为定量设计,包括7项前瞻性设计和13项回顾性设计。此外,还纳入了1篇定性研究、1篇混合方法研究、6篇综述文章和2篇来自机构网站的文章。与患者相关的益处被系统化分为六个主要类别:(1)预测,(2)高危患者的早期治疗和优先排序,(3)个体化治疗,(4)改善患者预后(包括改善序贯器官衰竭评估评分,缩短住院时间和降低死亡率),(5)总体改善护理,(6)降低再入院率。结论:这一范围综述强调了基于人工智能的CDSS对患者相关益处的积极影响的潜力,特别是在脓毒症治疗中,它们在改善重症监护方面表现出相当大的希望。然而,大多数已确定的研究依赖于回顾性数据库分析。未来的研究应侧重于通过前瞻性研究来验证这些发现。
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引用次数: 0
Feasibility, Acceptability, and Perspectives Regarding the Use of Activity Tracking Wearable Devices Among Home Health Aides: Mixed Methods Study. 在家庭健康助手中使用活动跟踪可穿戴设备的可行性、可接受性和前景:混合方法研究。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-26 DOI: 10.2196/77510
Ian René Solano-Kamaiko, Michael Dicpinigaitis, Melissa Tan, Irene Yang, Kexin Cheng, Ronica Peramsetty, Michelle Shum, Yanira Escamilla, Jennifer Bayly, Meghan Reading Turchioe, Ariel Avgar, Aditya Vashistha, Nicola Dell, Madeline R Sterling
<p><strong>Background: </strong>Home health aides and attendants (HHAs) provide in-home care to the growing population of older adults who want to age in place. Despite their vital role in patient care, HHAs are an underserved and vulnerable population of health care professionals who often experience poor health themselves. Activity tracking devices offer a promising way to improve HHAs' health-related awareness and promote health behavior change, particularly regarding physical activity and sleep quality, 2 areas in which the workforce struggles.</p><p><strong>Objective: </strong>This study aimed to understand how feasible it is for HHAs to use activity tracking devices and assess their perceptions of such devices for improving their health. Specifically, we conducted (1) a field study to assess the use, feasibility, and acceptability of these devices among HHAs and (2) a qualitative study to understand HHAs' perspectives on and reactions to activity trackers on and off the job.</p><p><strong>Methods: </strong>We partnered with the 1199 Service Employees International Union Training and Employment Fund to conduct a field study with home care agency-employed HHAs working in New York City, New York. Participants wore activity tracking devices for 4 weeks that collected data on physical activity and sleep. The HHAs were subsequently interviewed on their experiences with and attitudes toward the devices and asked to reflect on personalized visualizations of their data to prompt them to think aloud. Quantitative data were analyzed using descriptive statistics. Qualitative data were analyzed using grounded theory.</p><p><strong>Results: </strong>A total of 17 HHAs participated; their mean age was 48.7 (SD 12.2) years, 15 (88%) were women, 11 (65%) identified as Black, 5 (29%) identified as Hispanic or Latinx, and they had worked as HHAs for a mean of 11.7 (SD 7.5) years. In total, 94% (n=16) of the HHAs wore their activity trackers for the full 28-day study period. Participants took a mean of 10,230 (SD 3586) daily steps during the study period and slept for a mean of 6.27 (SD 0.58) hours per night. Overall, 4 key themes emerged: (1) activity tracking devices enhanced participants' health awareness by providing empirical data for self-reflection; (2) this increased awareness led to positive behavior changes, including setting and achieving health-related goals; (3) HHAs believed that these devices could improve not only their own health but also that of their patients through positive behavior changes; and (4) despite this optimism, participants emphasized that their ability to modify sleep and activity patterns was constrained by social and occupational determinants, with sleep improvements being particularly challenging.</p><p><strong>Conclusions: </strong>Our findings suggest that appropriately designed personal tracking interventions could offer a promising approach to supporting positive health-related changes in this historically overlooked wor
背景:家庭健康助手和护理人员(HHAs)为越来越多想在家中养老的老年人提供家庭护理。尽管他们在病人护理中发挥着至关重要的作用,但卫生保健专业人员是一个服务不足的弱势群体,他们自己的健康状况往往很差。活动跟踪设备提供了一种很有希望的方式来提高卫生保健机构的健康意识,促进健康行为的改变,特别是在体力活动和睡眠质量方面,这两个领域是劳动力的斗争。目的:本研究旨在了解hha使用活动跟踪设备的可行性,并评估他们对这种设备改善健康状况的看法。具体来说,我们进行了(1)一项实地研究,以评估这些设备在hha中的使用、可行性和可接受性;(2)一项定性研究,以了解hha在工作中和工作外对活动跟踪器的看法和反应。方法:我们与1199服务雇员国际工会培训和就业基金合作,对在纽约市工作的家庭护理机构雇用的HHAs进行实地研究。参与者佩戴活动追踪设备4周,收集身体活动和睡眠数据。随后,对hha进行了采访,询问他们使用这些设备的经历和态度,并要求他们对数据的个性化可视化进行反思,以促使他们大声思考。定量资料采用描述性统计进行分析。定性数据分析采用扎根理论。结果:共有17名hha参与;他们的平均年龄为48.7岁(SD 12.2),女性15人(88%),黑人11人(65%),西班牙裔或拉丁裔5人(29%),他们作为hha工作的平均时间为11.7年(SD 7.5)。总的来说,94% (n=16)的hha患者在整个28天的研究期间都佩戴了活动追踪器。在研究期间,参与者平均每天走10230步(SD 3586),平均每晚睡眠6.27小时(SD 0.58)。总体而言,出现了4个关键主题:(1)活动跟踪设备通过提供自我反思的经验数据来增强参与者的健康意识;(2)这种意识的增强导致了积极的行为改变,包括设定和实现与健康相关的目标;(3) HHAs相信这些设备不仅可以改善他们自己的健康,还可以通过积极的行为改变来改善患者的健康;(4)尽管乐观,但参与者强调,他们改变睡眠和活动模式的能力受到社会和职业决定因素的限制,改善睡眠尤其具有挑战性。结论:我们的研究结果表明,适当设计的个人跟踪干预措施可以提供一种有希望的方法来支持这一历史上被忽视的劳动力中与健康相关的积极变化,潜在地改善他们的福祉和他们为患者提供的护理质量。
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引用次数: 0
Assessing Health Care Professionals' Perceptions of a New System in Clinical Workflows: Systems Engineering Initiative for Patient Safety-Based Consensual Qualitative Research. 评估卫生保健专业人员对临床工作流程中新系统的看法:基于患者安全的共识定性研究的系统工程倡议。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-23 DOI: 10.2196/86166
Ye-Eun Park, Minsu Ock, Jae-Ho Lee, Dae-Hyun Ko, Hak-Jae Lee, Taezoon Park, Junsang Yoo, Yura Lee

Background: Artificial intelligence (AI)-enabled clinical decision support systems (CDSSs) are increasingly embedded within electronic health record (EHR) environments; however, their introduction can disrupt existing workflows and raise patient safety concerns, particularly in high-stakes settings such as surgical transfusion. Limited qualitative evidence exists regarding how frontline professionals anticipate the clinical, organizational, and workflow implications of such systems before wider deployment.

Objective: This study aims to qualitatively examine the anticipated clinical, organizational, and workflow-level implications of implementing personalized Maximum Surgical Blood Order Schedule-Thoracic Surgery (pMSBOS-TS), an AI-enabled CDSS for personalized surgical blood ordering, before large-scale deployment.

Methods: We conducted a consensual qualitative study with 14 multidisciplinary health care professionals involved in transfusion-related tasks at a large tertiary hospital. Following 1 pilot focus group to refine the interview guide and workflow diagram, 2 semistructured focus group discussions were held with 14 participants (5 physicians, 6 nurses, and 3 blood bank staff). Transcripts were analyzed using the Systems Engineering Initiative for Patient Safety (SEIPS) 101 framework, focusing on People, Environment, Tools, and Tasks, and were supported by task- and workflow-based analyses of transfusion processes. Member checking was conducted with participants and external clinicians to enhance validity.

Results: A total of 189 semantic units and 61 core ideas were identified across 18 subdomains and 7 overarching domains. Participants anticipated that pMSBOS-TS could reduce unwarranted variation in blood ordering and planning, provided that algorithmic performance is reliable and the interface is tightly integrated into existing EHR workflows. At the same time, they expressed concerns regarding increased verification burden, system limitations in unexpected clinical scenarios, and potential communication bottlenecks between clinical units and the blood bank. Organizational culture, governance structures, and local transfusion logistics were viewed as critical determinants of whether the system would reduce or inadvertently increase workload and blood product waste.

Conclusions: This preimplementation, SEIPS-based qualitative evaluation suggests that the successful adoption of an AI-enabled transfusion CDSS depends not only on predictive performance but also on sociotechnical readiness, including user trust, workflow fit, and organizational support. These findings provide practice-based insights to inform staged implementation, training, and governance strategies aimed at safely integrating predictive transfusion CDSSs into EHR-supported surgical workflows.

背景:支持人工智能(AI)的临床决策支持系统(cdss)越来越多地嵌入到电子健康记录(EHR)环境中;然而,它们的引入可能会扰乱现有的工作流程,并引起患者安全问题,特别是在外科输血等高风险环境中。关于一线专业人员如何在更广泛的部署之前预测这种系统的临床、组织和工作流程的影响,存在有限的定性证据。目的:本研究旨在定性地检验在大规模部署之前实施个性化最大外科血单计划-胸外科(pMSBOS-TS)的预期临床、组织和工作流程层面的影响,pMSBOS-TS是一种用于个性化外科血单的人工智能支持的CDSS。方法:我们对一家大型三级医院从事输血相关工作的14名多学科卫生保健专业人员进行了一项共识性质的研究。在1个试点焦点小组完善访谈指南和工作流程图之后,进行了2个半结构化焦点小组讨论,共有14名参与者(5名医生、6名护士和3名血库工作人员)。使用患者安全系统工程计划(SEIPS) 101框架分析转录本,重点关注人员、环境、工具和任务,并通过基于任务和工作流程的输血过程分析提供支持。与参与者和外部临床医生进行成员检查以提高有效性。结果:在18个子领域和7个总体领域中,共识别出189个语义单位和61个核心概念。与会者预计,pMSBOS-TS可以减少血液排序和计划中的不必要变化,前提是算法性能可靠,接口与现有的电子病历工作流程紧密集成。与此同时,他们对增加的核查负担、意外临床情况下的系统限制以及临床单位与血库之间潜在的沟通瓶颈表示关注。组织文化、治理结构和当地输血物流被视为该系统是否会减少或无意中增加工作量和血液制品浪费的关键决定因素。结论:这种预实施、基于seips的定性评估表明,人工智能支持的输血CDSS的成功采用不仅取决于预测性能,还取决于社会技术准备,包括用户信任、工作流程契合度和组织支持。这些发现提供了基于实践的见解,为分阶段实施、培训和治理策略提供信息,旨在将预测性输血cdss安全地整合到ehr支持的外科工作流程中。
{"title":"Assessing Health Care Professionals' Perceptions of a New System in Clinical Workflows: Systems Engineering Initiative for Patient Safety-Based Consensual Qualitative Research.","authors":"Ye-Eun Park, Minsu Ock, Jae-Ho Lee, Dae-Hyun Ko, Hak-Jae Lee, Taezoon Park, Junsang Yoo, Yura Lee","doi":"10.2196/86166","DOIUrl":"10.2196/86166","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI)-enabled clinical decision support systems (CDSSs) are increasingly embedded within electronic health record (EHR) environments; however, their introduction can disrupt existing workflows and raise patient safety concerns, particularly in high-stakes settings such as surgical transfusion. Limited qualitative evidence exists regarding how frontline professionals anticipate the clinical, organizational, and workflow implications of such systems before wider deployment.</p><p><strong>Objective: </strong>This study aims to qualitatively examine the anticipated clinical, organizational, and workflow-level implications of implementing personalized Maximum Surgical Blood Order Schedule-Thoracic Surgery (pMSBOS-TS), an AI-enabled CDSS for personalized surgical blood ordering, before large-scale deployment.</p><p><strong>Methods: </strong>We conducted a consensual qualitative study with 14 multidisciplinary health care professionals involved in transfusion-related tasks at a large tertiary hospital. Following 1 pilot focus group to refine the interview guide and workflow diagram, 2 semistructured focus group discussions were held with 14 participants (5 physicians, 6 nurses, and 3 blood bank staff). Transcripts were analyzed using the Systems Engineering Initiative for Patient Safety (SEIPS) 101 framework, focusing on People, Environment, Tools, and Tasks, and were supported by task- and workflow-based analyses of transfusion processes. Member checking was conducted with participants and external clinicians to enhance validity.</p><p><strong>Results: </strong>A total of 189 semantic units and 61 core ideas were identified across 18 subdomains and 7 overarching domains. Participants anticipated that pMSBOS-TS could reduce unwarranted variation in blood ordering and planning, provided that algorithmic performance is reliable and the interface is tightly integrated into existing EHR workflows. At the same time, they expressed concerns regarding increased verification burden, system limitations in unexpected clinical scenarios, and potential communication bottlenecks between clinical units and the blood bank. Organizational culture, governance structures, and local transfusion logistics were viewed as critical determinants of whether the system would reduce or inadvertently increase workload and blood product waste.</p><p><strong>Conclusions: </strong>This preimplementation, SEIPS-based qualitative evaluation suggests that the successful adoption of an AI-enabled transfusion CDSS depends not only on predictive performance but also on sociotechnical readiness, including user trust, workflow fit, and organizational support. These findings provide practice-based insights to inform staged implementation, training, and governance strategies aimed at safely integrating predictive transfusion CDSSs into EHR-supported surgical workflows.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e86166"},"PeriodicalIF":6.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12881895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
When Lived Experience Designs the Intervention. 当生活经验设计干预。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-23 DOI: 10.2196/91371
Trevor van Mierlo
{"title":"When Lived Experience Designs the Intervention.","authors":"Trevor van Mierlo","doi":"10.2196/91371","DOIUrl":"10.2196/91371","url":null,"abstract":"","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e91371"},"PeriodicalIF":6.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12829630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Data Poisoning Vulnerabilities Across Health Care Artificial Intelligence Architectures: Analytical Security Framework and Defense Strategies. 医疗保健人工智能架构中的数据中毒漏洞:分析安全框架和防御策略。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-23 DOI: 10.2196/87969
Farhad Abtahi, Fernando Seoane, Ivan Pau, Mario Vega-Barbas
<p><strong>Background: </strong>Health care artificial intelligence (AI) systems are increasingly integrated into clinical workflows, yet remain vulnerable to data-poisoning attacks. A small number of manipulated training samples can compromise AI models used for diagnosis, documentation, and resource allocation. Existing privacy regulations, including the Health Insurance Portability and Accountability Act and the General Data Protection Regulation, may inadvertently complicate anomaly detection and cross-institutional auditing, thereby limiting visibility into adversarial activity.</p><p><strong>Objective: </strong>This study provides a comprehensive threat analysis of data poisoning vulnerabilities across major health care AI architectures. The goals are to (1) identify attack surfaces in clinical AI systems, (2) evaluate the feasibility and detectability of poisoning attacks analytically modeled in prior security research, and (3) propose a multilayered defense framework appropriate for health care settings.</p><p><strong>Methods: </strong>We synthesized empirical findings from 41 key security studies published between 2019 and 2025 and integrated them into an analytical threat-modeling framework specific to health care. We constructed 8 hypothetical yet technically grounded attack scenarios across 4 categories: (1) architecture-specific attacks on convolutional neural networks, large language models, and reinforcement learning agents (scenario A); (2) infrastructure exploitation in federated learning and clinical documentation pipelines (scenario B); (3) poisoning of critical resource allocation systems (scenario C); and (4) supply chain attacks affecting commercial foundation models (scenario D). Scenarios were aligned with realistic insider-access threat models and current clinical deployment practices.</p><p><strong>Results: </strong>Multiple empirical studies demonstrate that attackers with access to as few as 100-500 poisoned samples can compromise health care AI systems, with attack success rates typically ≥60%. Critically, attack success depends on the absolute number of poisoned samples rather than their proportion of the training corpus, a finding that fundamentally challenges assumptions that larger datasets provide inherent protection. We estimate that detection delays commonly range from 6 to 12 months and may extend to years in distributed or privacy-constrained environments. Analytical scenarios highlight that (1) routine insider access creates numerous injection points across health care data infrastructure, (2) federated learning amplifies risks by obscuring attribution, and (3) supply chain compromises can simultaneously affect dozens to hundreds of institutions. Privacy regulations further complicate cross-patient correlation and model audit processes, substantially delaying the detection of subtle poisoning campaigns.</p><p><strong>Conclusions: </strong>Health care AI systems face significant security challenges that curre
背景:医疗保健人工智能(AI)系统越来越多地集成到临床工作流程中,但仍然容易受到数据中毒攻击。少量被操纵的训练样本可能会损害用于诊断、文档和资源分配的人工智能模型。现有的隐私法规,包括《健康保险流通与责任法案》和《一般数据保护条例》,可能会无意中使异常检测和跨机构审计复杂化,从而限制对敌对活动的可见性。目的:本研究对主要医疗保健AI架构中的数据中毒漏洞进行了全面的威胁分析。目标是(1)识别临床人工智能系统中的攻击面,(2)评估先前安全研究中分析建模的中毒攻击的可行性和可检测性,以及(3)提出适合医疗保健环境的多层防御框架。方法:我们综合了2019年至2025年间发表的41项关键安全研究的实证结果,并将其整合到针对医疗保健的分析性威胁建模框架中。我们构建了8个假设但技术上有根据的攻击场景,分为4类:(1)针对卷积神经网络、大型语言模型和强化学习代理(场景A)的特定架构攻击;(2)联邦学习和临床文档管道中的基础设施开发(场景B);(3)关键资源分配系统中毒(方案C);(4)影响商业基础模型的供应链攻击(场景D)。场景与现实的内部访问威胁模型和当前的临床部署实践保持一致。结果:多项实证研究表明,攻击者只要获得100-500个有毒样本,就能破坏医疗保健人工智能系统,攻击成功率通常≥60%。至关重要的是,攻击成功取决于中毒样本的绝对数量,而不是它们在训练语料库中的比例,这一发现从根本上挑战了大数据集提供固有保护的假设。我们估计,检测延迟通常在6到12个月之间,在分布式或隐私受限的环境中可能会延长至数年。分析情景强调:(1)常规的内部访问在医疗保健数据基础设施中创建了许多注入点;(2)联合学习通过模糊归因放大了风险;(3)供应链妥协可能同时影响数十到数百家机构。隐私法规进一步使跨患者相关性和模型审计过程复杂化,大大延迟了对微妙中毒活动的检测。结论:卫生保健人工智能系统面临着当前监管框架和验证实践无法充分解决的重大安全挑战。我们提出了一种多层防御策略,该策略结合了集成分歧监测、对抗性测试、隐私保护但可审计的机制以及加强的治理要求。确保患者安全可能需要从不透明的高性能模型转向更具可解释性和约束驱动的架构,并具有可验证的鲁棒性保证。
{"title":"Data Poisoning Vulnerabilities Across Health Care Artificial Intelligence Architectures: Analytical Security Framework and Defense Strategies.","authors":"Farhad Abtahi, Fernando Seoane, Ivan Pau, Mario Vega-Barbas","doi":"10.2196/87969","DOIUrl":"10.2196/87969","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Health care artificial intelligence (AI) systems are increasingly integrated into clinical workflows, yet remain vulnerable to data-poisoning attacks. A small number of manipulated training samples can compromise AI models used for diagnosis, documentation, and resource allocation. Existing privacy regulations, including the Health Insurance Portability and Accountability Act and the General Data Protection Regulation, may inadvertently complicate anomaly detection and cross-institutional auditing, thereby limiting visibility into adversarial activity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study provides a comprehensive threat analysis of data poisoning vulnerabilities across major health care AI architectures. The goals are to (1) identify attack surfaces in clinical AI systems, (2) evaluate the feasibility and detectability of poisoning attacks analytically modeled in prior security research, and (3) propose a multilayered defense framework appropriate for health care settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We synthesized empirical findings from 41 key security studies published between 2019 and 2025 and integrated them into an analytical threat-modeling framework specific to health care. We constructed 8 hypothetical yet technically grounded attack scenarios across 4 categories: (1) architecture-specific attacks on convolutional neural networks, large language models, and reinforcement learning agents (scenario A); (2) infrastructure exploitation in federated learning and clinical documentation pipelines (scenario B); (3) poisoning of critical resource allocation systems (scenario C); and (4) supply chain attacks affecting commercial foundation models (scenario D). Scenarios were aligned with realistic insider-access threat models and current clinical deployment practices.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Multiple empirical studies demonstrate that attackers with access to as few as 100-500 poisoned samples can compromise health care AI systems, with attack success rates typically ≥60%. Critically, attack success depends on the absolute number of poisoned samples rather than their proportion of the training corpus, a finding that fundamentally challenges assumptions that larger datasets provide inherent protection. We estimate that detection delays commonly range from 6 to 12 months and may extend to years in distributed or privacy-constrained environments. Analytical scenarios highlight that (1) routine insider access creates numerous injection points across health care data infrastructure, (2) federated learning amplifies risks by obscuring attribution, and (3) supply chain compromises can simultaneously affect dozens to hundreds of institutions. Privacy regulations further complicate cross-patient correlation and model audit processes, substantially delaying the detection of subtle poisoning campaigns.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Health care AI systems face significant security challenges that curre","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e87969"},"PeriodicalIF":6.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12881903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146029966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Diagnostic Performance of Deep Learning and Radiomics in Extracranial Carotid Plaque Detection: Systematic Review and Meta-Analysis. 深度学习和放射组学在颅外颈动脉斑块检测中的诊断性能:系统回顾和荟萃分析。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-22 DOI: 10.2196/77092
Lingjie Ju, Yongsheng Guo, Haiyong Guo, Ruijuan Liu, Yiyang Wang, Siyu Wang, Na Ma, Junhong Ren
<p><strong>Background: </strong>Artificial intelligence-enhanced imaging techniques have demonstrated promising diagnostic potential for carotid plaques, a key cardiovascular and cerebrovascular risk factor. However, previous studies did not systematically synthesize their diagnostic accuracy.</p><p><strong>Objective: </strong>This study aimed to quantitatively explore the diagnostic efficacy of deep learning (DL) and radiomics for extracranial carotid plaques and establish a standardized framework for improving plaque detection.</p><p><strong>Methods: </strong>We searched the PubMed, Embase, Cochrane, Web of Science, and Institute of Electrical and Electronics Engineers databases to identify studies involving the use of radiomics or DL models to diagnose extracranial carotid artery plaques from inception up to September 24, 2025. The quality of the studies was determined using Quality Assessment of Diagnostic Accuracy Studies for Artificial Intelligence (QUADAS-AI). A meta-analysis was conducted using StataMP (version 17.0; StataCorp) with a bivariate mixed-effects model to calculate pooled sensitivity and specificity, generate summary receiver operating characteristic (SROC) curves, assess Cochran Q statistic and I²-based heterogeneity, and conduct subgroup analyses and regression analysis.</p><p><strong>Results: </strong>Among 40 studies comprising 17,246 patients, 34 integrated independent test sets or validation sets in the quantitative statistical analysis. Among them, 24 focused on DL models, 10 on machine learning models based on radiomics. The combined sensitivity, specificity, and area under the SROC curve were 0.88 (95% CI 0.85-0.91; P<.001; I2=93.58%), 0.89 (95% CI 0.85-0.92; P<.001; I2=91.38%), and 0.95 (95% CI 0.92-0.96), respectively. Compared with the machine learning models based on radiomics algorithms, DL models achieved comparable improvements in specificity and area under the SROC curve. It was observed that transfer learning and a large sample size enhanced the diagnostic performance of models. Models used to identify plaque stability and presence had similar diagnostic performances, both of which were more effective in identifying symptomatic plaque models. A total of 7 studies demonstrated that the models that combined clinical features exhibited comparable diagnostic capability to pure DL and radiomics models. Additionally, 7 studies performed external validation, obtaining lower diagnostic performance than in testing groups. Limited regression analysis failed to identify significant sources of heterogeneity, and the limited number of eligible studies restricted more comprehensive subgroup analyses. The high heterogeneity in the study results may be due to different scanning parameters, model architecture, image segmentation, and algorithms.</p><p><strong>Conclusions: </strong>Radiomics algorithms and DL models can effectively diagnose extracranial carotid plaque. However, there are concerns regarding irregularities in re
背景:人工智能增强成像技术已经证明了对颈动脉斑块(一种关键的心脑血管危险因素)的诊断潜力。然而,以往的研究并没有系统地综合其诊断准确性。目的:本研究旨在定量探讨深度学习(deep learning, DL)和放射组学对颈动脉颅外斑块的诊断效果,建立提高斑块检测的标准化框架。方法:我们检索了PubMed、Embase、Cochrane、Web of Science和美国电气与电子工程师协会的数据库,以确定从成立到2025年9月24日使用放射组学或DL模型诊断颅外颈动脉斑块的研究。使用人工智能诊断准确性研究质量评估(QUADAS-AI)确定研究的质量。采用StataMP (version 17.0; StataCorp)软件进行meta分析,采用双变量混合效应模型计算合并敏感性和特异性,生成总受试者工作特征(SROC)曲线,评估Cochran Q统计量和基于I²的异质性,并进行亚组分析和回归分析。结果:40项研究共17246例患者,在定量统计分析中有34个综合独立试验集或验证集。其中,24个专注于深度学习模型,10个专注于基于放射组学的机器学习模型。综合敏感性、特异性和SROC曲线下面积为0.88 (95% CI 0.85-0.91)。结论:放射组学算法和DL模型可有效诊断颈动脉颅外斑块。然而,人们对研究设计的不规则性和缺乏多中心研究和外部验证感到担忧。未来的研究应以降低偏倚风险、增强模型的普遍性和临床导向为目标。
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引用次数: 0
UnitedXR Europe 2025: Aligning Health Care Extended Reality. unedxr欧洲2025:调整医疗保健扩展现实。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-21 DOI: 10.2196/90727
Jose Ferrer Costa
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引用次数: 0
SMARTCLOTH Prototype for Dietary Management in Patients With Diabetes Mellitus: Tutorial on Human-Centered Design Methodology for Health Care Hardware Development. 糖尿病患者饮食管理的SMARTCLOTH原型:卫生保健硬件开发的以人为本的设计方法教程。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-21 DOI: 10.2196/75744
Jose M Palomares, Rafael Molina-Luque, Fernando León-García, Irene Casares-Rodríguez, María García-Rodríguez, María Pilar Villena Esponera, Guillermo Molina-Recio
<p><strong>Background: </strong>Developing user-centered digital health hardware requires systematic design methods applicable across clinical contexts. As diabetes mellitus continues to rise globally and contributes to morbidity, mortality, and costs, effective nutritional management remains essential-yet adherence is often poor. Digital health interventions grounded in human-centered design may enhance adherence by better aligning solutions with patients' real needs.</p><p><strong>Objective: </strong>This tutorial aims to provide replicable guidance on applying the design thinking approach to health care hardware development, illustrated through the design, development, and preliminary usability evaluation of SMARTCLOTH (GA-16: Lifestyles, Innovation, and Health), a smart tablecloth prototype intended to facilitate dietary management and support adherence to nutritional recommendations among individuals with diabetes.</p><p><strong>Methods: </strong>We demonstrate a systematic design thinking approach adaptable to other hardware contexts, using the Double Diamond model. In mapping, we performed a structured preassessment to define project scope and feasible functionalities. To characterize end user needs, we conducted 6 in-depth interviews with health care professionals and applied persona, empathy map, and customer journey map tools. In exploring, 5 focus groups (patients and diabetes educators) identified barriers, facilitators, and desired functionalities for dietary self-management. In building, we created low- and high-fidelity wireframes and interactive web prototypes using Phaser 3 (HTML5/JS) to simulate a kitchen workspace for meal assembly. In testing, 7 patients with different diabetes profiles participated in 3 iterative usability sessions. Using think-aloud, video analysis, and structured tasks, we documented completion times, errors, and the level of required assistance, enabling refinements. Development progressed through 15 internal versions and 3 user-tested prototypes with real-time adjustments when feasible.</p><p><strong>Results: </strong>Interviews and focus groups yielded three user profiles guiding design: (1) adolescents with type 1 diabetes navigating social and dietary challenges, (2) working-age adults with type 2 diabetes who were motivated but inconsistent, and (3) older adults with type 2 diabetes showing low adherence due to entrenched habits. Iterative usability testing indicated that the system was intuitive, with improvements in layout, labeling, and navigation. Quantitative metrics showed refinement, with simple tasks being completed in under 1 minute in later iterations, while complex meal simulations took longer. Error rates and required guidance decreased as prototypes evolved. Qualitative feedback highlighted clarity, motivational value, and educational potential, while older participants requested larger text and simplified controls. Despite usability gains, motivational barriers persisted among low-adhere
背景:开发以用户为中心的数字医疗硬件需要适用于临床环境的系统设计方法。随着糖尿病在全球范围内持续上升,并导致发病率、死亡率和成本增加,有效的营养管理仍然至关重要,但坚持性往往很差。基于以人为本设计的数字健康干预措施可以通过更好地将解决方案与患者的实际需求结合起来,从而提高依从性。目的:本教程旨在通过SMARTCLOTH (GA-16:生活方式、创新和健康)的设计、开发和初步可用性评估,为将设计思维方法应用于医疗硬件开发提供可复制的指导。SMARTCLOTH是一种智能桌布原型,旨在促进糖尿病患者的饮食管理和坚持营养建议。方法:我们展示了一种系统的设计思维方法,适用于其他硬件环境,使用双钻石模型。在映射中,我们执行了结构化的预评估,以定义项目范围和可行的功能。为了描述最终用户的需求,我们对医疗保健专业人员进行了6次深入访谈,并应用了人物角色、移情图和客户旅程图工具。在探索中,5个焦点小组(患者和糖尿病教育者)确定了饮食自我管理的障碍、促进因素和期望功能。在构建中,我们使用Phaser 3 (HTML5/JS)创建了低保真和高保真线框和交互式web原型,以模拟厨房工作空间的用餐组装。在测试中,7名不同糖尿病患者参与了3次迭代可用性测试。使用有声思考、视频分析和结构化任务,我们记录了完成时间、错误和所需帮助的级别,从而实现了改进。开发经历了15个内部版本和3个用户测试原型,并在可行时进行了实时调整。结果:访谈和焦点小组得出了三种用户概况指导设计:(1)1型糖尿病青少年应对社会和饮食挑战;(2)2型糖尿病工作年龄成年人有动力但不一致;(3)2型糖尿病老年人由于根深蒂固的习惯而表现出较低的依从性。迭代的可用性测试表明,该系统是直观的,在布局、标签和导航方面都有改进。定量度量显示了改进,在后来的迭代中,简单的任务在1分钟内完成,而复杂的膳食模拟需要更长的时间。随着原型的发展,错误率和所需的指导减少了。定性反馈强调了清晰度、激励价值和教育潜力,而年长的参与者则要求更大的文本和简化的控制。尽管可用性有所提高,但在低依从性的老年人中,动机障碍仍然存在。结论:本教程展示了系统的以人为中心的设计可以产生可行且被广泛接受的数字健康硬件。SMARTCLOTH成为糖尿病患者饮食管理的一种很有前景的工具,尽管其有效性和临床结果尚未得到评估。开发慢性病管理硬件的团队可以采用这种方法。
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引用次数: 0
WHOOP, There It Is: Lessons From WHOOP's FDA Warning Letter. WHOOP,它在那里:从WHOOP的FDA警告信中吸取的教训。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-21 DOI: 10.2196/90882
Blythe Karow
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引用次数: 0
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Journal of Medical Internet Research
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