Frontiers in digital health最新文献

{"title":"Self-help psychological intervention for young individuals during the post-COVID-19 era: development of a PST chatbot using GPT-4.","authors":"Liuling Mo, He Li, Yanbo Zhang, Ang Li, Ziyue Xiong, Peixin Cun, Tingshao Zhu","doi":"10.3389/fdgth.2025.1627268","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1627268","url":null,"abstract":"<p><strong>Introduction: </strong>The COVID-19 pandemic has exacerbated psychological stress among young people, with some survivors experiencing persistent mental distress, thus creating an urgent need for accessible psychological intervention tools. To help young people affected by COVID-19 recover and achieve balanced mental health in the post-pandemic era, this study developed an online self-help psychological intervention chatbot to supplement existing mental health resources.</p><p><strong>Methods: </strong>We utilized prompt engineering techniques to construct a chatbot proficient in Problem-Solving Therapy (PST) based on the large language model GPT-4. Subsequently, 7 master's students majoring in psychological counseling were recruited for a pre-test of the chatbot, and 100 young people who had contracted COVID-19 were selected for a formal user experiment to evaluate its effectiveness.</p><p><strong>Results: </strong>The pre-test results indicated that the chatbot followed the core steps of PST during interactions with users and was helpful in problem-solving. The formal experiment showed that the experimental group scored significantly higher than the control group in the dimensions of problem awareness [<i>t</i> (88.31) = 3.14, <i>p</i> = 0.002] and problem-solving [<i>t</i> (98) = 3.34, <i>p</i> = 0.001], but there was no significant difference between the two groups in the dimension of relationship quality [<i>t</i> (91.23) = 1.07, <i>p</i> = 0.286]. In addition, no significant differences were found in the evaluation based on gender or the presence of post-COVID-19 symptoms, indicating that the chatbot has a certain degree of universal applicability.</p><p><strong>Conclusions: </strong>These findings support the application of the PST chatbot in post-COVID-19 era psychological interventions, particularly in assisting users with identifying problems and exploring solutions. Although the chatbot did not achieve significant improvement in human-computer relationship quality, its general acceptability and broad applicability demonstrate great potential in the field of mental health, highlighting the value of large language models in promoting self-help mental health interventions as a supplementary tool to existing resources.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1627268"},"PeriodicalIF":3.2,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253967","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}

{"title":"AI reshaping life sciences: intelligent transformation, application challenges, and future convergence in neuroscience, biology, and medicine.","authors":"Jiahuan Gong, Zihao Zhao, Xinxin Niu, Yanan Ji, Hualin Sun, Yuntian Shen, Bingqian Chen, Bei Wu","doi":"10.3389/fdgth.2025.1666415","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1666415","url":null,"abstract":"<p><p>The rapid advancement of artificial intelligence (AI) is profoundly transforming research paradigms and clinical practices across neuroscience, biology, and medicine with unprecedented depth and breadth. Leveraging its robust data-processing capabilities, precise pattern recognition techniques, and efficient real-time decision support, AI has catalyzed a paradigm shift toward intelligent, precision-oriented approaches in scientific research and healthcare. This review comprehensively reviews core AI applications within these domains. Within neuroscience, AI advances encompass brain-computer interface (BCI) development/optimization, intelligent analysis of neuroimaging data (e.g., fMRI, EEG), and early prediction/precise diagnosis of neurological disorders. In biological research, AI applications include enhanced gene-editing efficiency (e.g., CRISPR) with off-target effect prediction, genomic big-data interpretation, drug discovery/design (e.g., virtual screening), high-accuracy protein structure prediction (exemplified by AlphaFold), biodiversity monitoring, and ecological conservation strategy optimization. For medical research, AI empowers auxiliary medical image diagnosis (e.g., CT, MRI), pathological analysis, personalized treatment planning, health risk prediction with lifespan health management, and robot-assisted minimally invasive surgery (e.g., da Vinci Surgical System). This review not only synthesizes AI's pivotal role in enhancing research efficiency and overcoming limitations of conventional methodologies, but also critically examines persistent challenges, including data access barriers, algorithmic non-transparency, ethical governance gaps, and talent shortages. Building upon this analysis, we propose a tripartite framework (\"Technology-Ethics-Talent\") to advance intelligent transformation in scientific and medical domains. Through coordinated implementation, AI will catalyze a transition toward efficient, accessible, and sustainable healthcare, ultimately establishing a life-cycle preservation paradigm encompassing curative gene editing, proactive health management, and ecologically intelligent governance.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1666415"},"PeriodicalIF":3.2,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500557/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253993","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}

{"title":"Cautious optimism: public voices on medical AI and sociotechnical harm.","authors":"Beverley A Townsend, Victoria J Hodge, Hannah Richardson, Radu Calinescu, T T Arvind","doi":"10.3389/fdgth.2025.1625747","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1625747","url":null,"abstract":"<p><strong>Background: </strong>Medical-purpose software and Artificial Intelligence (\"AI\")-enabled technologies (\"medical AI\") raise important social, ethical, cultural, and regulatory challenges. To elucidate these important challenges, we present the findings of a qualitative study undertaken to elicit public perspectives and expectations around medical AI adoption and related sociotechnical harm. Sociotechnical harm refers to any adverse implications including, but not limited to, physical, psychological, social, and cultural impacts experienced by a person or broader society as a result of medical AI adoption. The work is intended to guide effective policy interventions to address, prioritise, and mitigate such harm.</p><p><strong>Methods: </strong>Using a qualitative design approach, twenty interviews and/or long-form questionnaires were completed between September and November 2024 with UK participants to explore their perspectives, expectations, and concerns around medical AI adoption and related sociotechnical harm. An emphasis was placed on diversity and inclusion, with study participants drawn from racially, ethnically, and linguistically diverse groups and from self-identified minority groups. A thematic analysis of interview transcripts and questionnaire responses was conducted to identify general medical AI perception and sociotechnical harm.</p><p><strong>Results: </strong>Our findings demonstrate that while participants are cautiously optimistic about medical AI adoption, all participants expressed concern about matters related to sociotechnical harm. This included potential harm to human autonomy, alienation and a reduction in standards of care, the lack of value alignment and integration, epistemic injustice, bias and discrimination, and issues around access and equity, explainability and transparency, and data privacy and data-related harm. While responsibility was seen to be shared, participants located responsibility for addressing sociotechnical harm primarily with the regulatory authorities. An identified concern was risk of exclusion and inequitable access on account of practical barriers such as physical limitations, technical competency, language barriers, or financial constraints.</p><p><strong>Conclusion: </strong>We conclude that medical AI adoption can be better supported through identifying, prioritising, and addressing sociotechnical harm including the development of clear impact and mitigation practices, embedding pro-social values within the system, and through effective policy guidance intervention.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1625747"},"PeriodicalIF":3.2,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253919","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}

{"title":"Exploring health professionals' views on the depiction of conversational agents as health professionals: a qualitative descriptive study.","authors":"A Luke MacNeill, Lillian MacNeill, Alison Luke, Shelley Doucet","doi":"10.3389/fdgth.2025.1590514","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1590514","url":null,"abstract":"<p><strong>Background: </strong>Some health care conversational agents (HCCAs) are designed to simulate health professionals in terms of their presentation or appearance. Research suggests that the public has favorable views toward the depiction of HCCAs as health professionals, but the views of health professionals are less clear. We conducted a qualitative descriptive study to learn more about health professionals' views on this topic.</p><p><strong>Methods: </strong>Physicians, nurses, and regulated mental health professionals were recruited using web-based methods. Participants were interviewed individually using the Zoom videoconferencing platform. They were asked to discuss potential benefits and drawbacks surrounding the depiction of HCCAs as health professionals. Interviews were transcribed verbatim and uploaded to NVivo (version 12; QSR International, Inc) for thematic analysis.</p><p><strong>Results: </strong>Twenty-four health professionals participated in the study (19 women, five men; <i>M</i> age = 42.75 years, <i>SD</i> = 10.71). Three themes were developed from their interview data. Participants said that portraying HCCAs as health professionals is a form of misrepresentation and may mislead program users. Participants were also concerned that these depictions could draw from stereotypes regarding the appearance of health professionals, which might affect people's expectations surrounding these programs or their willingness to use them. Despite these concerns, some participants thought that there may be benefits to depicting HCCAs as health professionals, particularly in terms of providing a sense of reassurance to people seeking health support.</p><p><strong>Conclusions: </strong>The health professionals in this study expressed mixed views toward the depiction of HCCAs as health professionals. Their insights may prompt further discussion on the appropriate depiction of HCCAs among developers and other stakeholders.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1590514"},"PeriodicalIF":3.2,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12497840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245874","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}

{"title":"Editorial: The scale-up and sustainability of digital health interventions in low- and middle-income settings.","authors":"Bassey Ebenso, Eve Namisango, Ibukun-Oluwa Abejirinde, Matthew J Allsop","doi":"10.3389/fdgth.2025.1634223","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1634223","url":null,"abstract":"","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1634223"},"PeriodicalIF":3.2,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12497710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245884","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}

{"title":"TRACE: applying AI language models to extract ancestry information from curated biomedical literature.","authors":"Alison M Veintimilla, Chintan K Acharya, Connie J Mulligan, Ruogu Fang, Erika Moore","doi":"10.3389/fdgth.2025.1608370","DOIUrl":"10.3389/fdgth.2025.1608370","url":null,"abstract":"<p><strong>Introduction: </strong>Ancestry reporting is essential to ensure transparency and proper representation in biomedical studies. However, manually extracting this information from study texts is time-consuming and inefficient. In this paper, we present TRACE (Tool for Researching Ancestry and Cell Extraction), powered by GPT-4 and web-crawling, to automate ancestry identification by detecting cell lines or cultures in texts and tracing their ancestry.</p><p><strong>Methods: </strong>TRACE extracts cell lines and primary cultures from research articles and follows web sources to determine their ancestry. We compared TRACE's outputs to a manually generated database to confirm its performance in identifying and verifying ancestry information.</p><p><strong>Results: </strong>The results reveal an overrepresentation of European/White samples and significant underreporting. TRACE enables large-scale, systematic ancestry analysis-a valuable resource for researchers and agencies assessing biases in sample selection.</p><p><strong>Conclusions: </strong>As an open-source tool, TRACE it facilitates broader use to evaluate and improve ancestry representation in biomedical research.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1608370"},"PeriodicalIF":3.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234257","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}

{"title":"Increasing access to care through digital health for the Medicaid population: a novel community case study.","authors":"Melinda Cooling, Colleen J Klein, Matthew D Dalstrom, Roopa Foulger, Jennifer Junis, Jonathan A Handler","doi":"10.3389/fdgth.2025.1524590","DOIUrl":"10.3389/fdgth.2025.1524590","url":null,"abstract":"<p><p>There is a growing consensus among healthcare professionals and policymakers that the way healthcare has historically been provided within the United States is insufficient to meet the needs of the population. The incidence and prevalence of many chronic diseases, coupled with the challenges associated with accessing prenatal care, are notable across the country and globally. In response to this problem OSF HealthCare and four federally qualified health centers partnered together to reimagine how health care can be delivered to underserved populations. This case study provides a practical perspective on how care delivery is enhanced, delivered, and improved through use of digital technologies to expand access to care and chronic disease management in the Medicaid population. Through the formation of the Medicaid Innovation Collaborative, which is partially funded by the Illinois Department of Health and Family Services, digital health programs tailored to individual patient needs and supported by remote and in-person digital health navigators (DHNs), are provided with 24/7/365 access to care from a diverse team of healthcare professionals. In this article, we describe the essential program elements, design, and implementation of four novel programs. While developing digital care solutions for adult Medicaid recipients across the state has been challenging, our work illustrates the feasibility of such an endeavor. To date, we have outreached to over 418,037 patients, and enrolled 38,964 in our diverse programs that include, but are not limited to, helping patients managing chronic disease, increasing access to prenatal care, offering support for health literacy and wellness, and screening for the social determinants of health.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1524590"},"PeriodicalIF":3.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234232","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}

{"title":"DISCOVER: a Data-driven Interactive System for Comprehensive Observation, Visualization, and ExploRation of human behavior.","authors":"Tobias Hallmen, Dominik Schiller, Antonia Vehlen, Steffen Eberhardt, Tobias Baur, Daksitha Withanage Don, Wolfgang Lutz, Elisabeth André","doi":"10.3389/fdgth.2025.1638539","DOIUrl":"10.3389/fdgth.2025.1638539","url":null,"abstract":"<p><p>Understanding human behavior is a fundamental goal of social sciences, yet conventional methodologies are often limited by labor-intensive data collection and complex analyses. Computational models offer a promising alternative for analyzing large datasets and identifying key behavioral indicators, but their adoption is hindered by technical complexity and substantial computational requirements. To address these barriers, we introduce <i>DISCOVER</i>, a modular and user-friendly software framework designed to streamline computational data exploration for human behavior analysis. <i>DISCOVER</i> democratizes access to state-of-the-art models, enabling researchers across disciplines to conduct detailed behavioral analyses without extensive technical expertise. In this paper, we are showcasing <i>DISCOVER</i> using four modular data exploration workflows that build on each other: Semantic Content Exploration, Visual Inspection, Aided Annotation, and Multimodal Scene Search. Finally, we report initial findings from a user study. The study examined <i>DISCOVER</i>'s potential to support prospective psychotherapists in structuring information for treatment planning, i.e. case conceptualizations.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1638539"},"PeriodicalIF":3.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492635/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234285","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}

{"title":"Prevention of sudden unexpected postnatal collapse in wellbeing newborns by remote digital health technologies.","authors":"Massimo Berger, Adalberto Brach Del Prever, Michele Mario Calvo, Roberto Bellino, Davide Gallina, Fabio Stefano Timeus, Fabrizio Bogliatto","doi":"10.3389/fdgth.2025.1598541","DOIUrl":"10.3389/fdgth.2025.1598541","url":null,"abstract":"<p><strong>Introduction: </strong>To prevent the Sudden Unexpected Postnatal Collapse (SUPC) this approach was carried out. SUPC is a rare and devastating event for the child and their family. Currently, no diagnostic prediction model is available to calculate the individual newborn risk.</p><p><strong>Patient and methods: </strong>To prevent SUPC, the Department of Maternal and Child Health at ASLTO4 in Piedmont, Northern Italy, has implemented wireless cardiopulmonary monitoring for all newborns during the first 24 h of life, starting on June 10th, 2023, to December 31st, 2024. The study involved approximately 2,000 newborns from three Spoke hospitals in Northern Italy. The aim of the study was to evaluate the feasibility of wireless monitoring in a large series of newborns.</p><p><strong>Results: </strong>On more than 2,000 newborns, we have seen parental refusal in only two cases. The system was well accepted by the families after adequate explanation of the monitoring modalities and its meaning. The wireless system has in no way hindered the skin-to-skin moment nor delayed the time of attachment to the breast and the usual neonatal screening procedures. The introduction of this new technology has brought increased serenity to parents, especially in situations of severe tiredness after troubled births or after cesarean delivery. As a very preliminary results in 2,250 newborns the monitoring system detected various pathological events, in particular two cases of SUPC which were promptly resuscitated without subsequent neurological sequelae.</p><p><strong>Conclusions: </strong>We report on our proof-of-concept innovative digital approach to intercept SUPCs as soon as possible. Through this study we want to demonstrate that it is possible to carry out large-scale multicenter monitoring, without interfering with breast attachment and the initial mother-infant relationship. The limitations of the study mainly concern the fact that this monitoring was carried out on term or late pre-term infants. This was due to the unavailability of a neonatal intensive care (TIN) within our hospitals and therefore severe preterm children were born or transferred early to a third level hospital.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1598541"},"PeriodicalIF":3.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234219","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}

{"title":"Impact of integrating guidelines into an antimicrobial stewardship smartphone application on outpatient antibiotic prescribing: a segmented interrupted time series analysis.","authors":"Ahmed A Sadeq, Laila Z Alhaj Ali, Jinan M Shamseddine, Barbara R Conway, Stuart E Bond, Rizwan Ali, William J Lattyak, Zahir Osman Eltahir Babiker, Mamoon A Aldeyab","doi":"10.3389/fdgth.2025.1647528","DOIUrl":"10.3389/fdgth.2025.1647528","url":null,"abstract":"<p><strong>Introduction: </strong>Antimicrobial stewardship (AMS) smartphone applications (apps) have been adopted to promote better antimicrobial prescribing practices. We aimed to evaluate the impact of incorporating an app on AMS metrics and adherence to a local antimicrobial guideline in an outpatient setting.</p><p><strong>Methods: </strong>A quasi-experimental, segmented interrupted time series design was used, involving three study phases (pre-intervention: 1st January 2020 to 31st December 2021; implementation: 1st January 2022 to 31st December 2022, and post-intervention: 1st January 2023 to 30th June 2024) in a hospital outpatient setting. The effect of introducing an AMS app incorporating local antimicrobial guidelines on AMS outcomes was measured.</p><p><strong>Results: </strong>A total of 24,424 patients were identified. As per the most simple model, the amounts of the following antibiotics, expressed as defined daily dose (DDD) per 100 patient visits, increased significantly during the post-intervention phase: azithromycin (co-efficient 0.297, <i>p</i> = 0.007), co-amoxiclav (co-efficient 2.608, <i>p</i> = 0.042), and nitrofurantoin (co-efficient 0.908, <i>p</i> = 0.003). The trend in fosfomycin use decreased significantly in the post-intervention phase (co-efficient -0.23., <i>p</i> < 0.001). Guideline adherence increased significantly after implementing the AMS app (trend change co-efficient 0.011, <i>p</i> < 0.001). These changes in antibiotic prescribing represent improved guideline adherence, and are aligned with WHO AWaRe categorisation recommendations.</p><p><strong>Conclusion: </strong>The app improved the utilization of antibiotic prescribing by increasing adherence to local antimicrobial guidelines, affirming its utility in augmenting AMS in outpatient settings.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1647528"},"PeriodicalIF":3.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208480","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}