<p><strong>Background: </strong>Artificial intelligence (AI)-enabled wearable devices are rapidly emerging in rehabilitation and motor function assessment for patients with Parkinson disease (PD). However, evidence remains fragmented, integration into nursing practice is limited, and comprehensive synthesis is lacking.</p><p><strong>Objective: </strong>This study aimed to summarize studies on AI-enabled wearable devices for PD rehabilitation and motor function assessment, describing device types, monitored indicators, algorithms, and application characteristics, and identifying research gaps and barriers to clinical translation.</p><p><strong>Methods: </strong>Guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework, 9 databases (China National Knowledge Infrastructure, Wanfang Data, SinoMed, Cochrane Library, PubMed, Web of Science, CINAHL, Scopus, and Embase) were searched from inception to December 2025. Eligible studies were published in English or Chinese from January 1, 2020, onward and enrolled people with PD using noninvasive, body-worn AI-enabled wearable devices for rehabilitation, assessment, or monitoring. Dissertations and full conference papers were included, whereas preprints and conference abstracts were excluded. Methodological quality was appraised using the Mixed Methods Appraisal Tool, 2018 tool. Results were synthesized narratively and mapped to characterize devices, sensing modalities, algorithms, and evaluation methods.</p><p><strong>Results: </strong>A total of 66 studies involving approximately 3579 participants were included. Wearable devices mainly comprised multisensor modules, smart insoles, and wrist-worn devices, with accelerometers being the most frequently used sensors. Data collection was predominantly passive, and most studies were conducted in laboratory or clinical settings using single- or short-term sessions. Internal validation approaches, particularly leave-one-out and k-fold cross-validation, were common, whereas external validation was rare, and reporting of calibration and clinical decision thresholds was limited. Sensitivity and accuracy were the most frequently reported performance metrics, highlighting substantial heterogeneity in analytical methods and outcome reporting.</p><p><strong>Conclusions: </strong>This scoping review systematically synthesized evidence on AI-enabled wearable devices for motor function assessment and rehabilitation in PD, complemented by an evidence map and guided by a rehabilitation- and nursing-oriented perspective, and identified key translational gaps between proof-of-concept studies and real-world rehabilitation workflows. Compared with previous reviews that primarily focused on monitoring functions or device performance, this review places greater emphasis on rehabilitation applications and nurse-led translation into practice, and proposes a conceptual "challenges and opportunities" framework
背景：支持人工智能（AI）的可穿戴设备在帕金森病（PD）患者的康复和运动功能评估中迅速兴起。然而，证据仍然是碎片化的，整合到护理实践是有限的，缺乏全面的综合。目的：本研究旨在总结人工智能可穿戴设备在PD康复和运动功能评估方面的研究，描述设备类型、监测指标、算法和应用特点，并找出研究空白和临床转化的障碍。方法：在PRISMA-ScR （Preferred Reporting Items for Systematic Reviews and meta - analysis extension for Scoping Reviews）框架的指导下，检索自成立至2025年12月的9个数据库（中国国家知识基础设施、万方数据、中国医学信息中心、Cochrane图书馆、PubMed、Web of Science、CINAHL、Scopus和Embase）。符合条件的研究从2020年1月1日起以英文或中文发表，并招募PD患者使用无创、穿戴式ai可穿戴设备进行康复、评估或监测。包括学位论文和完整的会议论文，而不包括预印本和会议摘要。方法质量评价使用混合方法评价工具，2018年工具。结果以叙述的方式合成并映射到表征设备，传感模式，算法和评估方法。结果：共纳入66项研究，涉及约3579名受试者。可穿戴设备主要包括多传感器模块、智能鞋垫和腕戴设备，其中加速度计是最常用的传感器。数据收集主要是被动的，大多数研究在实验室或临床环境中进行，使用单次或短期会议。内部验证方法，特别是留一和k倍交叉验证，是常见的，而外部验证是罕见的，校准和临床决策阈值的报告是有限的。灵敏度和准确性是最常报告的性能指标，突出了分析方法和结果报告的实质性异质性。结论：本综述系统地综合了人工智能可穿戴设备用于PD患者运动功能评估和康复的证据，辅以证据图，并以康复和护理为导向的视角为指导，并确定了概念验证研究与现实世界康复工作流程之间的关键转化差距。与以往主要关注监测功能或设备性能的综述相比，本综述更加强调康复应用和护士主导的实践转化，并提出了一个概念性的“挑战和机遇”框架，为设备和算法的设计、评估和报告提供信息，同时进一步强调了工作流集成和决策支持系统实施的关键考虑因素。这些发现对于促进临床、家庭和社区环境下康复的连续性具有实际意义，并可能有助于指导护士提供持续监测、个性化随访和及时干预，从而提高康复管理的效率和可及性。
{"title":"AI-Enabled Wearables for Motor Function Assessment and Rehabilitation in Parkinson Disease: Scoping Review.","authors":"Shengting Li, Siqi Chen, Xiaosong Yu, Huixiang Shang, Tingting Tu, Mingtao Quan","doi":"10.2196/85596","DOIUrl":"10.2196/85596","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI)-enabled wearable devices are rapidly emerging in rehabilitation and motor function assessment for patients with Parkinson disease (PD). However, evidence remains fragmented, integration into nursing practice is limited, and comprehensive synthesis is lacking.</p><p><strong>Objective: </strong>This study aimed to summarize studies on AI-enabled wearable devices for PD rehabilitation and motor function assessment, describing device types, monitored indicators, algorithms, and application characteristics, and identifying research gaps and barriers to clinical translation.</p><p><strong>Methods: </strong>Guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework, 9 databases (China National Knowledge Infrastructure, Wanfang Data, SinoMed, Cochrane Library, PubMed, Web of Science, CINAHL, Scopus, and Embase) were searched from inception to December 2025. Eligible studies were published in English or Chinese from January 1, 2020, onward and enrolled people with PD using noninvasive, body-worn AI-enabled wearable devices for rehabilitation, assessment, or monitoring. Dissertations and full conference papers were included, whereas preprints and conference abstracts were excluded. Methodological quality was appraised using the Mixed Methods Appraisal Tool, 2018 tool. Results were synthesized narratively and mapped to characterize devices, sensing modalities, algorithms, and evaluation methods.</p><p><strong>Results: </strong>A total of 66 studies involving approximately 3579 participants were included. Wearable devices mainly comprised multisensor modules, smart insoles, and wrist-worn devices, with accelerometers being the most frequently used sensors. Data collection was predominantly passive, and most studies were conducted in laboratory or clinical settings using single- or short-term sessions. Internal validation approaches, particularly leave-one-out and k-fold cross-validation, were common, whereas external validation was rare, and reporting of calibration and clinical decision thresholds was limited. Sensitivity and accuracy were the most frequently reported performance metrics, highlighting substantial heterogeneity in analytical methods and outcome reporting.</p><p><strong>Conclusions: </strong>This scoping review systematically synthesized evidence on AI-enabled wearable devices for motor function assessment and rehabilitation in PD, complemented by an evidence map and guided by a rehabilitation- and nursing-oriented perspective, and identified key translational gaps between proof-of-concept studies and real-world rehabilitation workflows. Compared with previous reviews that primarily focused on monitoring functions or device performance, this review places greater emphasis on rehabilitation applications and nurse-led translation into practice, and proposes a conceptual \"challenges and opportunities\" framework ","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e85596"},"PeriodicalIF":6.0,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12982951/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147290176","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}
{"title":"Wearable Air Samplers Reveal How Wildfire Shapes the Exposome.","authors":"Virginia Gewin","doi":"10.2196/93193","DOIUrl":"10.2196/93193","url":null,"abstract":"","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e93193"},"PeriodicalIF":6.0,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12940450/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147307022","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}
Linda K Truong, James G Wrightson, Raphaël Vincent, Eunice Lui, Jamon L Couch, Ellen Wang, Cobie Starcevich, Dean Giustini, Alex Haagaard, Elena Lopatina, Niels van Berkel, Michael Skovdal Rathleff, Clare L Ardern
{"title":"Correction: Evidence for Digital Health Tools Designed to Support the Triage of Musculoskeletal Conditions in Primary, Urgent, and Emergency Care Settings: Scoping Review.","authors":"Linda K Truong, James G Wrightson, Raphaël Vincent, Eunice Lui, Jamon L Couch, Ellen Wang, Cobie Starcevich, Dean Giustini, Alex Haagaard, Elena Lopatina, Niels van Berkel, Michael Skovdal Rathleff, Clare L Ardern","doi":"10.2196/92722","DOIUrl":"10.2196/92722","url":null,"abstract":"","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e92722"},"PeriodicalIF":6.0,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12945097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147307013","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}
<p><strong>Background: </strong>Adolescents perceive both immediate and long-term benefits and losses related to internet gaming, affecting their risk of internet gaming disorder (IGD). These perceptions could also be shaped and reinforced by IGD, indicating potential bidirectionality.</p><p><strong>Objective: </strong>This study aimed to investigate the bidirectional relationships between perceived immediate and long-term benefits in 3 domains (mental health, social relationships, and personal achievement) and IGD, and between perceived immediate and long-term losses in 6 domains (mental health, sleep quality, academic performance, family relationships, social relationships, and personal achievement) and IGD.</p><p><strong>Methods: </strong>A 12-month 2-wave prospective longitudinal study was conducted among junior middle school students who had played internet games in the past 12 months in Guangzhou and Chengdu, China, with a baseline survey (T1, December 2018) and the other identical follow-up survey conducted 1 year later (T2, December 2019). The participating schools were conveniently selected; all Grade 7 and 8 students were invited to self-administer the questionnaires in a classroom setting without the presence of the schoolteachers. The final sample size was 1173 students (mean age 12.5, SD 0.6 y; male: 693/1173, 59.1%). IGD was assessed by using the 9-item Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition IGD checklist.</p><p><strong>Results: </strong>Cross-lagged panel analysis (adjusting for background factors) showed (1) stronger perceived immediate benefits of mental health (β=.08, 95% CI 0.01-0.15) and personal achievement (β=.10, 95% CI 0.01-0.20) at T1 significantly predicted more IGD symptoms at T2; (2) more IGD symptoms at T1 significantly predicted stronger perceived immediate and long-term benefits of social relationships (immediate: β=.09, 95% CI 0.03-0.15; long-term: β=.11, 95% CI:0.05-0.17) and personal achievement (immediate: β=.12, 95% CI 0.06-0.18; long-term: β=.10, 95% CI 0.04-0.16) at T2; (3) more IGD symptoms at T1 significantly predicted stronger perceived immediate and future losses in mental health (immediate: β=.09, 95% CI 0.03-0.15; long-term: β=.08, 95% CI 0.02-0.14), sleep quality (immediate: β=.10, 95% CI 0.04-0.16; long-term: β=.13, 95% CI 0.07-0.19), academic performance (immediate: β=.09, 95% CI 0.04-0.15; long-term: β=.07, 95% CI 0.01-0.13), and family relationships (immediate: β=.11, 95% CI 0.05-0.17; long-term: β=.10, 95% CI 0.04-0.16) at T2, as well as perceived long-term losses in social relationships at T2 (β=.08, 95% CI 0.02-0.14).</p><p><strong>Conclusions: </strong>This study was innovative in integrating time perspective into both perceived benefits and losses of internet gaming, a cognitive dimension previously overlooked in literature. The current findings advance the field by revealing the unidimensional predictive effects of IGD on perceived immediate and long-term benefit
背景：青少年认为与网络游戏相关的即时和长期利益和损失影响着他们患网络游戏障碍（IGD）的风险。这些观念也可以由IGD塑造和加强，表明潜在的双向性。目的：本研究旨在探讨心理健康、社会关系和个人成就3个领域的近期收益和长期收益与IGD的双向关系，以及心理健康、睡眠质量、学习成绩、家庭关系、社会关系和个人成就6个领域的近期损失和长期损失与IGD的双向关系。方法：对过去12个月在中国广州和成都玩过网络游戏的初中生进行为期12个月的两波前瞻性纵向研究，其中基线调查（T1, 2018年12月）和另一项相同的随访调查（T2, 2019年12月）。参与学校是方便挑选的；所有七年级和八年级的学生都被邀请在没有老师在场的教室里自行填写问卷。最终样本量为1173名学生（平均年龄12.5，标准差0.6 y；男性：693/1173,59.1%）。使用《精神障碍诊断与统计手册》第5版IGD检查表对IGD进行评估。结果：交叉滞后面板分析（调整背景因素）显示：(1)T1时较强的心理健康（β= 0.08, 95% CI 0.01-0.15）和个人成就（β= 0.10, 95% CI 0.01-0.20）的即时获益显著预测T2时更多的IGD症状；(2) T1时更多的IGD症状显著预测T2时更强的即时和长期社会关系（即时：β= 0.09, 95% CI 0.03-0.15；长期：β= 0.11, 95% CI 0.05-0.17）和个人成就（即时：β= 0.12, 95% CI 0.06-0.18；长期：β= 0.10, 95% CI 0.04-0.16）；(3) T1时更多的IGD症状显著预示着心理健康（即时：β= 0.09, 95% CI 0.03-0.15；长期：β= 0.08, 95% CI 0.02-0.14）、睡眠质量（即时：β= 0.10, 95% CI 0.04-0.16；长期：β= 0.13, 95% CI 0.07-0.19）、学习成绩（即时：β= 0.09, 95% CI 0.04-0.15；长期：β= 0.07, 95% CI 0.01-0.13）和家庭关系(即时：β= 0.11, 95% CI 0.05-0.17；长期：β= 0.10, 95% CI 0.04-0.16)，以及T2时感知到的长期社会关系损失（β= 0.08, 95% CI 0.02-0.14）。结论：该研究在将时间视角整合到网络游戏的感知收益和损失方面具有创新性，这是一个之前被文献所忽视的认知维度。目前的研究结果通过揭示IGD对感知到的即时和长期收益和损失的单维预测作用推进了这一领域，但心理健康和个人成就的感知到的即时和长期收益相反地预测了IGD。这些结果有助于开发有效的干预措施：认知成分应该超越游戏的一般利弊，并针对玩家持有的潜在时间偏见。
{"title":"Bidirectionality Between Perceived Immediate and Long-Term Benefits and Losses and Internet Gaming Disorder Among Chinese Adolescent Gamers: Prospective Longitudinal Study.","authors":"Siman Li, Jianxin Zhang, Ji-Bin Li, Joseph Tf Lau, Yanqiu Yu","doi":"10.2196/74030","DOIUrl":"10.2196/74030","url":null,"abstract":"<p><strong>Background: </strong>Adolescents perceive both immediate and long-term benefits and losses related to internet gaming, affecting their risk of internet gaming disorder (IGD). These perceptions could also be shaped and reinforced by IGD, indicating potential bidirectionality.</p><p><strong>Objective: </strong>This study aimed to investigate the bidirectional relationships between perceived immediate and long-term benefits in 3 domains (mental health, social relationships, and personal achievement) and IGD, and between perceived immediate and long-term losses in 6 domains (mental health, sleep quality, academic performance, family relationships, social relationships, and personal achievement) and IGD.</p><p><strong>Methods: </strong>A 12-month 2-wave prospective longitudinal study was conducted among junior middle school students who had played internet games in the past 12 months in Guangzhou and Chengdu, China, with a baseline survey (T1, December 2018) and the other identical follow-up survey conducted 1 year later (T2, December 2019). The participating schools were conveniently selected; all Grade 7 and 8 students were invited to self-administer the questionnaires in a classroom setting without the presence of the schoolteachers. The final sample size was 1173 students (mean age 12.5, SD 0.6 y; male: 693/1173, 59.1%). IGD was assessed by using the 9-item Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition IGD checklist.</p><p><strong>Results: </strong>Cross-lagged panel analysis (adjusting for background factors) showed (1) stronger perceived immediate benefits of mental health (β=.08, 95% CI 0.01-0.15) and personal achievement (β=.10, 95% CI 0.01-0.20) at T1 significantly predicted more IGD symptoms at T2; (2) more IGD symptoms at T1 significantly predicted stronger perceived immediate and long-term benefits of social relationships (immediate: β=.09, 95% CI 0.03-0.15; long-term: β=.11, 95% CI:0.05-0.17) and personal achievement (immediate: β=.12, 95% CI 0.06-0.18; long-term: β=.10, 95% CI 0.04-0.16) at T2; (3) more IGD symptoms at T1 significantly predicted stronger perceived immediate and future losses in mental health (immediate: β=.09, 95% CI 0.03-0.15; long-term: β=.08, 95% CI 0.02-0.14), sleep quality (immediate: β=.10, 95% CI 0.04-0.16; long-term: β=.13, 95% CI 0.07-0.19), academic performance (immediate: β=.09, 95% CI 0.04-0.15; long-term: β=.07, 95% CI 0.01-0.13), and family relationships (immediate: β=.11, 95% CI 0.05-0.17; long-term: β=.10, 95% CI 0.04-0.16) at T2, as well as perceived long-term losses in social relationships at T2 (β=.08, 95% CI 0.02-0.14).</p><p><strong>Conclusions: </strong>This study was innovative in integrating time perspective into both perceived benefits and losses of internet gaming, a cognitive dimension previously overlooked in literature. The current findings advance the field by revealing the unidimensional predictive effects of IGD on perceived immediate and long-term benefit","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e74030"},"PeriodicalIF":6.0,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12945363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147307005","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}
Heike Vornhagen, Stephen Barrett, Ciara Carroll, Lydia Kavochi Iladiva, Gregory Martin, Declan McKeown, Jennifer Martin
Background: Health care dashboards have the potential to enhance understanding, decision-making, and communication. However, their design, implementation, and evaluation are often hindered by the absence of standardized guidelines. This scoping review synthesizes international evidence to identify common practices for health care dashboard design, providing a foundation for application in the Irish context.
Objective: This study aimed to identify existing guidelines and common practices for health care dashboard design to inform future development and implementation within the Irish health care system.
Methods: A scoping review using an evidence summary approach was conducted. PubMed, Embase, Scopus, and IEEE Xplore (2014-2024) were searched. Practices were extracted and analyzed using reflexive thematic analysis and then grouped into 4 main pillars: approach (engagement of end users and stakeholders), content (data quality, effective insights, and presentation), behavior (usability and accessibility), and adoption (sustainability).
Results: From 1644 initially identified studies, 18 (1.1%) met the inclusion criteria. Most were hospital focused (13/18, 72.2%), with few community- or public-facing dashboards. Only 4 of 18 (22.2%) studies described structured guidelines; most implementations (14/18, 77.8%) were ad hoc. Common practices included user involvement, actionable metrics, data quality, usability, and workflow integration. Divergences were observed: hospitals prioritized clinical indicators, public dashboards emphasized transparency, and community dashboards were underrepresented. Conflicting findings included debate over interactivity vs static simplicity.
Conclusions: Dashboard design remains fragmented, with limited guidance for structured design or implementation. The 4 pillars provide a practical synthesis of best practices, highlighting pathways for evidence-informed, user-centered design. These pillars will inform future consensus building and co-design of health care dashboards in Ireland and can serve as a foundation for broader application in primary care, community, and public health settings.
{"title":"Design Practices for Data Dashboards in Health Care: Scoping Review.","authors":"Heike Vornhagen, Stephen Barrett, Ciara Carroll, Lydia Kavochi Iladiva, Gregory Martin, Declan McKeown, Jennifer Martin","doi":"10.2196/77361","DOIUrl":"10.2196/77361","url":null,"abstract":"<p><strong>Background: </strong>Health care dashboards have the potential to enhance understanding, decision-making, and communication. However, their design, implementation, and evaluation are often hindered by the absence of standardized guidelines. This scoping review synthesizes international evidence to identify common practices for health care dashboard design, providing a foundation for application in the Irish context.</p><p><strong>Objective: </strong>This study aimed to identify existing guidelines and common practices for health care dashboard design to inform future development and implementation within the Irish health care system.</p><p><strong>Methods: </strong>A scoping review using an evidence summary approach was conducted. PubMed, Embase, Scopus, and IEEE Xplore (2014-2024) were searched. Practices were extracted and analyzed using reflexive thematic analysis and then grouped into 4 main pillars: approach (engagement of end users and stakeholders), content (data quality, effective insights, and presentation), behavior (usability and accessibility), and adoption (sustainability).</p><p><strong>Results: </strong>From 1644 initially identified studies, 18 (1.1%) met the inclusion criteria. Most were hospital focused (13/18, 72.2%), with few community- or public-facing dashboards. Only 4 of 18 (22.2%) studies described structured guidelines; most implementations (14/18, 77.8%) were ad hoc. Common practices included user involvement, actionable metrics, data quality, usability, and workflow integration. Divergences were observed: hospitals prioritized clinical indicators, public dashboards emphasized transparency, and community dashboards were underrepresented. Conflicting findings included debate over interactivity vs static simplicity.</p><p><strong>Conclusions: </strong>Dashboard design remains fragmented, with limited guidance for structured design or implementation. The 4 pillars provide a practical synthesis of best practices, highlighting pathways for evidence-informed, user-centered design. These pillars will inform future consensus building and co-design of health care dashboards in Ireland and can serve as a foundation for broader application in primary care, community, and public health settings.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e77361"},"PeriodicalIF":6.0,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12980066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147289909","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}
Abhishek Bazaz, Yunan Ji, Mariana P Socal, So-Yeon Kang
This study analyzes 2010-2024 venture capital trends in international artificial intelligence-driven biopharmaceutical startups, revealing rapid growth in discovery tool investments and concentrated US funding in California and Massachusetts.
{"title":"Trends in Venture Capital Investment in AI-Driven Biopharmaceutical Startups.","authors":"Abhishek Bazaz, Yunan Ji, Mariana P Socal, So-Yeon Kang","doi":"10.2196/84968","DOIUrl":"10.2196/84968","url":null,"abstract":"<p><p>This study analyzes 2010-2024 venture capital trends in international artificial intelligence-driven biopharmaceutical startups, revealing rapid growth in discovery tool investments and concentrated US funding in California and Massachusetts.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e84968"},"PeriodicalIF":6.0,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12980058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147290131","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}
Peyman Nejat, Ashley D Bachman, Vicki M Stubbs, Joseph R Duffy, John L Stricker, Vitaly Herasevich, David T Jones, Rene L Utianski, Hugo Botha
<p><strong>Background: </strong>Digital recruitment methods offer opportunities to address challenges in clinical research participation, particularly in neurology. However, the impact of digital approaches across socioeconomic and demographic groups remains inadequately understood.</p><p><strong>Objective: </strong>This study investigates the influence of sociodemographic factors on recruitment and attrition in a remote neurological research cohort, mapping participation pathways and identifying disparities to inform inclusive digital strategies.</p><p><strong>Methods: </strong>We conducted a nonexperimental, observational longitudinal cohort study at Mayo Clinic using patient-portal invitations between March and July 2024 as part of a remote speech capture study. Eligibility criteria included age 18 years and older, US residence, and English proficiency. Of 5846 invited patients, progression was tracked across checkpoints (invitation, eligibility screening, electronic consent, and task completion) using Epic (Epic Systems Corporation) to obtain demographic information, Qualtrics (Qualtrics, LLC) for screening, PTrax (a Mayo Clinic-developed Participant Tracking System) for consent tracking, and the recording platform. Socioeconomic context was assessed using the Housing-based Socioeconomic Status (HOUSES) index, where higher values indicate higher socioeconomic status, and the Area Deprivation Index (ADI), where higher values reflect greater neighborhood disadvantage. Data diagnostics included Anderson-Darling tests for non-normality and Little missing completely at random (MCAR) test to characterize missingness. Associations between participation outcomes and age, sex, urbanicity, and socioeconomic indices were examined using nonparametric tests. Exact P values and 95% CIs are reported. Analyses were conducted using BlueSky Statistics (BlueSky Statistics, LLC) and the Python SciPy package.</p><p><strong>Results: </strong>Overall, 415 out of 5846 participants (7.1%) completed all study requirements. Completers were older (median age 66.4, IQR 56.0-72.5; 95% CI 65.1-67.6 years) than noncompleters (median age 62.8, IQR 47.5-72.7; 95% CI 62.2-63.2 years; P<.001). Participants from more socioeconomically disadvantaged neighborhoods were less likely to respond (invitation nonresponder median ADI 45.0, IQR 29.0-63.0 vs interested median ADI 42.0, IQR 27.0-59.0; P<.001), and completers had slightly lower ADI ranks than noncompleters (median 41.0, IQR 27.0-56.0 vs median 44.5, IQR 28.0-62.0; P=.04). Urban participants enrolled faster (median 32.0, IQR 9.0-58.0; 95% CI 31.0-37.0 days) than rural (median 41.0, IQR 22.0-65.0; 95% CI 37.0-49.0 days; P=.01). Female participants responded slower (median 38.5, IQR 14.8-66.3; 95% CI 35.0-41.0 days) than males (median 32.0, IQR 8.0-57.5; 95% CI 29.0-38.0 days; P=.01). No significant differences were observed for the HOUSES index, and device type was unrelated to completion or timelines. Missingness for key vari
背景：数字化招聘方法为应对临床研究参与中的挑战提供了机会，特别是在神经病学领域。然而，数字方法对社会经济和人口群体的影响仍然没有得到充分的了解。目的：本研究调查了远程神经学研究队列中社会人口因素对招募和流失的影响，绘制了参与路径并确定了差异，为包容性数字战略提供了信息。方法：我们在梅奥诊所进行了一项非实验性的观察性纵向队列研究，使用2024年3月至7月期间的患者门户网站邀请作为远程语音捕获研究的一部分。资格标准包括18岁及以上，美国居民和英语水平。在5846名受邀患者中，使用Epic （Epic Systems Corporation）获取人口统计信息、Qualtrics （Qualtrics， LLC）进行筛查、PTrax（梅奥诊所开发的参与者跟踪系统）进行同意跟踪和记录平台，通过检查点（邀请、资格筛选、电子同意和任务完成）跟踪进展。社会经济背景的评估采用基于住房的社会经济地位指数（HOUSES）和区域剥夺指数（ADI），前者值越高表明社会经济地位越高，后者值越高反映社区劣势越大。数据诊断包括非正态性的安德森-达林检验和完全随机缺失（MCAR）检验来表征缺失。参与结果与年龄、性别、城市化程度和社会经济指标之间的关系采用非参数检验。报告了精确的P值和95% ci。使用BlueSky Statistics （BlueSky Statistics， LLC）和Python SciPy包进行分析。结果：总的来说，5846名参与者中有415名（7.1%）完成了所有研究要求。完成者的年龄（中位年龄66.4岁，IQR 56.0-72.5岁；95% CI 65.1-67.6岁）大于未完成者（中位年龄62.8岁，IQR 47.5-72.7岁；95% CI 62.2-63.2岁）。结论：数字化招聘不能克服传统的参与障碍，并可能引入与年龄、城市化和社区劣势相关的新差异。这些发现为包容性数字研究战略提供了信息，包括多渠道外展、特定年龄的参与和农村技术支持。本研究将纵向路径分析应用于数字神经学招聘，为提高远程研究的包容性提供可操作的见解。
{"title":"Sociodemographic Drivers of Recruitment and Attrition in Digital Neurological Research: Longitudinal Cohort Study.","authors":"Peyman Nejat, Ashley D Bachman, Vicki M Stubbs, Joseph R Duffy, John L Stricker, Vitaly Herasevich, David T Jones, Rene L Utianski, Hugo Botha","doi":"10.2196/83432","DOIUrl":"10.2196/83432","url":null,"abstract":"<p><strong>Background: </strong>Digital recruitment methods offer opportunities to address challenges in clinical research participation, particularly in neurology. However, the impact of digital approaches across socioeconomic and demographic groups remains inadequately understood.</p><p><strong>Objective: </strong>This study investigates the influence of sociodemographic factors on recruitment and attrition in a remote neurological research cohort, mapping participation pathways and identifying disparities to inform inclusive digital strategies.</p><p><strong>Methods: </strong>We conducted a nonexperimental, observational longitudinal cohort study at Mayo Clinic using patient-portal invitations between March and July 2024 as part of a remote speech capture study. Eligibility criteria included age 18 years and older, US residence, and English proficiency. Of 5846 invited patients, progression was tracked across checkpoints (invitation, eligibility screening, electronic consent, and task completion) using Epic (Epic Systems Corporation) to obtain demographic information, Qualtrics (Qualtrics, LLC) for screening, PTrax (a Mayo Clinic-developed Participant Tracking System) for consent tracking, and the recording platform. Socioeconomic context was assessed using the Housing-based Socioeconomic Status (HOUSES) index, where higher values indicate higher socioeconomic status, and the Area Deprivation Index (ADI), where higher values reflect greater neighborhood disadvantage. Data diagnostics included Anderson-Darling tests for non-normality and Little missing completely at random (MCAR) test to characterize missingness. Associations between participation outcomes and age, sex, urbanicity, and socioeconomic indices were examined using nonparametric tests. Exact P values and 95% CIs are reported. Analyses were conducted using BlueSky Statistics (BlueSky Statistics, LLC) and the Python SciPy package.</p><p><strong>Results: </strong>Overall, 415 out of 5846 participants (7.1%) completed all study requirements. Completers were older (median age 66.4, IQR 56.0-72.5; 95% CI 65.1-67.6 years) than noncompleters (median age 62.8, IQR 47.5-72.7; 95% CI 62.2-63.2 years; P<.001). Participants from more socioeconomically disadvantaged neighborhoods were less likely to respond (invitation nonresponder median ADI 45.0, IQR 29.0-63.0 vs interested median ADI 42.0, IQR 27.0-59.0; P<.001), and completers had slightly lower ADI ranks than noncompleters (median 41.0, IQR 27.0-56.0 vs median 44.5, IQR 28.0-62.0; P=.04). Urban participants enrolled faster (median 32.0, IQR 9.0-58.0; 95% CI 31.0-37.0 days) than rural (median 41.0, IQR 22.0-65.0; 95% CI 37.0-49.0 days; P=.01). Female participants responded slower (median 38.5, IQR 14.8-66.3; 95% CI 35.0-41.0 days) than males (median 32.0, IQR 8.0-57.5; 95% CI 29.0-38.0 days; P=.01). No significant differences were observed for the HOUSES index, and device type was unrelated to completion or timelines. Missingness for key vari","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e83432"},"PeriodicalIF":6.0,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12935417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147290009","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}
Melissa MacKay, Soroush Zamani Alavijeh, Sydney Gosselin, Fattane Zarrinkalam, Jennifer E McWhirter
<p><strong>Background: </strong>As Canada's climate changes, extreme heat events have become more frequent, a trend that is expected to continue. Extreme heat can lead to several negative health outcomes, which disproportionately impact vulnerable populations. Evidence-based, equitable interventions are needed to inform and protect the public from the health effects. Effective communication can aid this effort to improve health outcomes by emphasizing the connection between health risks and climate change and empowering people to act. Machine learning has applications in understanding current attitudes, beliefs, experiences, and behaviors within the target audience for public health messaging. Machine learning analyses of social media data have elucidated user perceptions of heat events in the literature; however, research is limited with respect to social media user perceptions, beliefs, and behaviors related to extreme heat, particularly in the Canadian context. Analyzing Canadian social media discourse related to extreme heat will help to address this research gap and inform future research and communications to reduce the risks of extreme heat.</p><p><strong>Objective: </strong>The purpose of this research is to better understand Canadian discourse and emotions related to extreme heat by examining social media (Reddit). Our objectives include (1) identifying common discussion topics, concerns, and questions related to extreme heat among Canadian Reddit users; (2) analyzing sentiment and emotional responses to extreme heat discussions; and (3) investigating the relationship between topics, sentiment, and engagement for posts.</p><p><strong>Methods: </strong>We collected data using the Reddit application programming interface (API), retrieving posts from 30 Canada-specific subreddits between February 12, 2023, and February 11, 2024, based on a predefined set of heat- and climate-related keywords. Posts and comments were structured as hierarchical tree models, with text consolidated into documents for analysis. Topic modeling, sentiment analysis, and emotion analysis were conducted; engagement was assessed using net upvote scores to gauge community approval.</p><p><strong>Results: </strong>The analysis of 607 Reddit posts from 15,366 users revealed that discussions about extreme heat were most frequently centered around the keyword "heat," which appeared in 82.5% (n=501) of the posts and 81.1% (n=25,253) of the comments. Topic analysis identified key themes related to heating and cooling costs, weather records, air conditioning, and health impacts, while sentiment and emotion analyses showed varying levels of positivity and negativity across subreddits.</p><p><strong>Conclusions: </strong>Our findings present an initial snapshot into Canadian perspectives and information needs about extreme heat in Canada. In our sample, discussions on Reddit about extreme heat in Canada are dominated by concerns over heating and cooling costs, weather patterns,
{"title":"Exploring Reddit Discourse and Information Needs Surrounding Extreme Heat: Topic, Sentiment, and Engagement Analysis.","authors":"Melissa MacKay, Soroush Zamani Alavijeh, Sydney Gosselin, Fattane Zarrinkalam, Jennifer E McWhirter","doi":"10.2196/82426","DOIUrl":"10.2196/82426","url":null,"abstract":"<p><strong>Background: </strong>As Canada's climate changes, extreme heat events have become more frequent, a trend that is expected to continue. Extreme heat can lead to several negative health outcomes, which disproportionately impact vulnerable populations. Evidence-based, equitable interventions are needed to inform and protect the public from the health effects. Effective communication can aid this effort to improve health outcomes by emphasizing the connection between health risks and climate change and empowering people to act. Machine learning has applications in understanding current attitudes, beliefs, experiences, and behaviors within the target audience for public health messaging. Machine learning analyses of social media data have elucidated user perceptions of heat events in the literature; however, research is limited with respect to social media user perceptions, beliefs, and behaviors related to extreme heat, particularly in the Canadian context. Analyzing Canadian social media discourse related to extreme heat will help to address this research gap and inform future research and communications to reduce the risks of extreme heat.</p><p><strong>Objective: </strong>The purpose of this research is to better understand Canadian discourse and emotions related to extreme heat by examining social media (Reddit). Our objectives include (1) identifying common discussion topics, concerns, and questions related to extreme heat among Canadian Reddit users; (2) analyzing sentiment and emotional responses to extreme heat discussions; and (3) investigating the relationship between topics, sentiment, and engagement for posts.</p><p><strong>Methods: </strong>We collected data using the Reddit application programming interface (API), retrieving posts from 30 Canada-specific subreddits between February 12, 2023, and February 11, 2024, based on a predefined set of heat- and climate-related keywords. Posts and comments were structured as hierarchical tree models, with text consolidated into documents for analysis. Topic modeling, sentiment analysis, and emotion analysis were conducted; engagement was assessed using net upvote scores to gauge community approval.</p><p><strong>Results: </strong>The analysis of 607 Reddit posts from 15,366 users revealed that discussions about extreme heat were most frequently centered around the keyword \"heat,\" which appeared in 82.5% (n=501) of the posts and 81.1% (n=25,253) of the comments. Topic analysis identified key themes related to heating and cooling costs, weather records, air conditioning, and health impacts, while sentiment and emotion analyses showed varying levels of positivity and negativity across subreddits.</p><p><strong>Conclusions: </strong>Our findings present an initial snapshot into Canadian perspectives and information needs about extreme heat in Canada. In our sample, discussions on Reddit about extreme heat in Canada are dominated by concerns over heating and cooling costs, weather patterns,","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e82426"},"PeriodicalIF":6.0,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12935418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147290037","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}
Ion Nemteanu, Adir Mancebo, Leslie Joe, Ryan Lopez, Patricia Lopez, Warren Woodrich Pettine
<p><strong>Unlabelled: </strong>Artificial intelligence (AI) is transforming patient care, but it also raises ethical questions, such as bias and transparency. While a range of well-established frameworks exist to guide responsible AI practice, most were designed for academic or regulatory settings and can be hard to operationalize within fast-moving, resource-limited small and medium-sized enterprises (SMEs). We report on the collaborative design of the SAFE-AI (Scalable Agile Framework for Execution in AI), an approach that embeds ethical safeguards, including fairness, transparency, responsibility metrics, and continuous monitoring, directly into standard Agile development cycles. In keeping with established Agile principles, SAFE-AI provides "just enough structure" to integrate ethical oversight into existing workflows without prescribing extensive new governance layers. Similar to other Agile frameworks, such as Scrum, which is described as a "lightweight framework" designed to help teams solve complex problems through iterative learning and minimal process overhead, SAFE-AI aims to remain practical for organizations that may not have dedicated ethics or compliance staff. Rather than simplifying technical methods, SAFE-AI simplifies when and how ethical review is triggered and documented, making responsible AI practices feasible even in environments with limited ethics, governance, or compliance resources. SAFE-AI assumes the presence of qualified data scientists and engineers, and it does not replace the need for statistical or technical expertise but instead provides a lightweight structure for coordinating and documenting work that those experts already perform. We followed a design-science, practice-oriented approach over 20 weeks. After a discovery workshop, a cross-functional team was assembled that included SME employees, ethics researchers, and academic partners. The SME's role was limited to informing design constraints and feasibility considerations during the co-design phase. No operational pilot or production deployment was conducted as part of this study. To reduce the risk of internal design bias and improve generalizability, we also consulted external stakeholders through structured feedback sessions, including clinicians, health care domain experts, and regulatory specialists. Their feedback was incorporated into each prototype-feedback cycle, ensuring that priorities reflected not only the SME's immediate context but also broader clinical and regulatory perspectives. The co-design process produced a 4-phase SAFE-AI life cycle: discovery, assessment, development, and monitoring. SAFE-AI's phase-specific checklists meld acceptance, fairness, and transparency metrics into each Agile sprint. A novel scenario-based probability analogy mapping method was added to translate model risk and uncertainty into plain-language narratives for nontechnical stakeholders, forming the framework's core "responsibility metrics" layer. SAFE-AI is
{"title":"Scalable Agile Framework for Execution in AI for Medical AI Ethics Policy Design in Small- and Medium-Sized Enterprises.","authors":"Ion Nemteanu, Adir Mancebo, Leslie Joe, Ryan Lopez, Patricia Lopez, Warren Woodrich Pettine","doi":"10.2196/80028","DOIUrl":"10.2196/80028","url":null,"abstract":"<p><strong>Unlabelled: </strong>Artificial intelligence (AI) is transforming patient care, but it also raises ethical questions, such as bias and transparency. While a range of well-established frameworks exist to guide responsible AI practice, most were designed for academic or regulatory settings and can be hard to operationalize within fast-moving, resource-limited small and medium-sized enterprises (SMEs). We report on the collaborative design of the SAFE-AI (Scalable Agile Framework for Execution in AI), an approach that embeds ethical safeguards, including fairness, transparency, responsibility metrics, and continuous monitoring, directly into standard Agile development cycles. In keeping with established Agile principles, SAFE-AI provides \"just enough structure\" to integrate ethical oversight into existing workflows without prescribing extensive new governance layers. Similar to other Agile frameworks, such as Scrum, which is described as a \"lightweight framework\" designed to help teams solve complex problems through iterative learning and minimal process overhead, SAFE-AI aims to remain practical for organizations that may not have dedicated ethics or compliance staff. Rather than simplifying technical methods, SAFE-AI simplifies when and how ethical review is triggered and documented, making responsible AI practices feasible even in environments with limited ethics, governance, or compliance resources. SAFE-AI assumes the presence of qualified data scientists and engineers, and it does not replace the need for statistical or technical expertise but instead provides a lightweight structure for coordinating and documenting work that those experts already perform. We followed a design-science, practice-oriented approach over 20 weeks. After a discovery workshop, a cross-functional team was assembled that included SME employees, ethics researchers, and academic partners. The SME's role was limited to informing design constraints and feasibility considerations during the co-design phase. No operational pilot or production deployment was conducted as part of this study. To reduce the risk of internal design bias and improve generalizability, we also consulted external stakeholders through structured feedback sessions, including clinicians, health care domain experts, and regulatory specialists. Their feedback was incorporated into each prototype-feedback cycle, ensuring that priorities reflected not only the SME's immediate context but also broader clinical and regulatory perspectives. The co-design process produced a 4-phase SAFE-AI life cycle: discovery, assessment, development, and monitoring. SAFE-AI's phase-specific checklists meld acceptance, fairness, and transparency metrics into each Agile sprint. A novel scenario-based probability analogy mapping method was added to translate model risk and uncertainty into plain-language narratives for nontechnical stakeholders, forming the framework's core \"responsibility metrics\" layer. SAFE-AI is","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e80028"},"PeriodicalIF":6.0,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12935426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147290004","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}
Martin Bastiaan Schilder, Alexandra Keyser, Susan van Hees, Alessandro Sbrizzi, Wouter Pieter Christiaan Boon
<p><strong>Background: </strong>Artificial intelligence (AI) promises to significantly impact daily radiology practices. Numerous studies have already been conducted that anticipate this potentially disruptive innovation. So far, these studies have mainly focused on single topics, such as "trust," or investigating perspectives of single stakeholder groups, such as "radiologists."</p><p><strong>Objective: </strong>This study aims to explore future directions for AI in radiology by incorporating perspectives of a heterogeneous group of stakeholders on a broad spectrum of moral and economic topics. It also aims to cocreate and reflect with a broad range of stakeholders on viable implementation scenarios for scalable AI applications in radiology in the Netherlands, thereby identifying potential opportunities and frictions, with a focus on moral and economic considerations.</p><p><strong>Methods: </strong>To inform the workshop design, a nonsystematic narrative literature search was performed to deepen our understanding of key moral and economic considerations at play in the field of radiology and AI. Workshop participants, representing a wide range of actors including radiologists, innovators, and patient representatives, were selected using purposive sampling. Data were collected in a cocreation workshop. In 3 subsequent rounds, mixed over 3 breakout groups, a total of 17 participants were asked to (1) map what they considered important moral and economic considerations, (2) envision possible future scenarios for AI in radiology, and (3) discuss opportunities, frictions, and routes to success. Transcribed recordings were coded and cross-checked.</p><p><strong>Results: </strong>Workshop participants envision future AI-driven scenarios, ranging from extramural imaging departments for increased accessibility to health care, to multimodal data integration for human-centered AI-enhanced diagnostics. Seven themes emerge from the discussions during the workshop: (1) trust and efficiency of AI technologies, (2) responsibilities in clinical decision-making when AI is involved, (3) diagnosis as a one-off versus an iterative process, (4) regulations as a requirement or a restriction, (5) economic benefits or drawbacks, (6) trade-off between amount of information required and patient privacy, and (7) environmental considerations.</p><p><strong>Conclusions: </strong>Reflecting on the 7 emerging themes, we identify three overarching topics: (1) human-AI collaboration and trust, (2) governance, regulation, and ethical safeguards, and (3) value creation and sustainability. These topics highlight the need to balance technological advancements with ethical responsibility, institutional accountability, and societal benefit. They also underscore the importance of designing AI systems that not only perform well but are also trusted and aligned with clinical workflows and patient values. These overarching themes offer a lens through which future research and policy can n
{"title":"Anticipating Moral and Economic Considerations, Opportunities, and Potential Frictions for AI in Medical Imaging: Multistakeholder Cocreation Study.","authors":"Martin Bastiaan Schilder, Alexandra Keyser, Susan van Hees, Alessandro Sbrizzi, Wouter Pieter Christiaan Boon","doi":"10.2196/83407","DOIUrl":"10.2196/83407","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) promises to significantly impact daily radiology practices. Numerous studies have already been conducted that anticipate this potentially disruptive innovation. So far, these studies have mainly focused on single topics, such as \"trust,\" or investigating perspectives of single stakeholder groups, such as \"radiologists.\"</p><p><strong>Objective: </strong>This study aims to explore future directions for AI in radiology by incorporating perspectives of a heterogeneous group of stakeholders on a broad spectrum of moral and economic topics. It also aims to cocreate and reflect with a broad range of stakeholders on viable implementation scenarios for scalable AI applications in radiology in the Netherlands, thereby identifying potential opportunities and frictions, with a focus on moral and economic considerations.</p><p><strong>Methods: </strong>To inform the workshop design, a nonsystematic narrative literature search was performed to deepen our understanding of key moral and economic considerations at play in the field of radiology and AI. Workshop participants, representing a wide range of actors including radiologists, innovators, and patient representatives, were selected using purposive sampling. Data were collected in a cocreation workshop. In 3 subsequent rounds, mixed over 3 breakout groups, a total of 17 participants were asked to (1) map what they considered important moral and economic considerations, (2) envision possible future scenarios for AI in radiology, and (3) discuss opportunities, frictions, and routes to success. Transcribed recordings were coded and cross-checked.</p><p><strong>Results: </strong>Workshop participants envision future AI-driven scenarios, ranging from extramural imaging departments for increased accessibility to health care, to multimodal data integration for human-centered AI-enhanced diagnostics. Seven themes emerge from the discussions during the workshop: (1) trust and efficiency of AI technologies, (2) responsibilities in clinical decision-making when AI is involved, (3) diagnosis as a one-off versus an iterative process, (4) regulations as a requirement or a restriction, (5) economic benefits or drawbacks, (6) trade-off between amount of information required and patient privacy, and (7) environmental considerations.</p><p><strong>Conclusions: </strong>Reflecting on the 7 emerging themes, we identify three overarching topics: (1) human-AI collaboration and trust, (2) governance, regulation, and ethical safeguards, and (3) value creation and sustainability. These topics highlight the need to balance technological advancements with ethical responsibility, institutional accountability, and societal benefit. They also underscore the importance of designing AI systems that not only perform well but are also trusted and aligned with clinical workflows and patient values. These overarching themes offer a lens through which future research and policy can n","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e83407"},"PeriodicalIF":6.0,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12935424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147289897","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}
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