Journal of Medical Systems最新文献

{"title":"A Standardized Framework for Evaluating State Opioid Surveillance Dashboards: A Health Informatics Assessment.","authors":"Chance Aguiar, Maria Bolus, Seshadri C Mudumbai","doi":"10.1007/s10916-026-02399-w","DOIUrl":"10.1007/s10916-026-02399-w","url":null,"abstract":"<p><strong>Background: </strong>State public health agencies across the U.S. depend on opioid surveillance dashboards to monitor overdose trends and guide interventions. Despite their central role in decision-making, no standardized framework exists to evaluate how well these dashboards actually function.</p><p><strong>Objective: </strong>We developed a health informatics framework to systematically assess state opioid surveillance dashboards and applied it across all 50 U.S. states.</p><p><strong>Methods: </strong>Our framework evaluates dashboards across nine domains (including temporal analysis, geographic granularity, data disaggregation, visualization features, narrative support, simplification techniques, and interactive functionality) using 28 specific sub-variables. Sub-variables were selected based on public health informatics literature on effective dashboard design, including features identified in COVID-19 dashboard evaluations and usability frameworks. We scored each feature as either present or absent, creating composite scores with a theoretical maximum of 28 points. All 50 U.S. state-level dashboards were reviewed twice by the study team, with an external consistency check on a subset of 10 dashboards. We calculated the correlation coefficient between dashboard scores and state population estimates.</p><p><strong>Results: </strong>Dashboard scores varied widely, ranging from 12 to 22 points (mean:19.18, standard deviation:2.20). Three states (Delaware, Indiana, and New York) achieved the highest score of 22 by offering comprehensive geographic detail, extensive data breakdowns, and full interactive capabilities. Interactive filtering was present in 92% of dashboards, but only 80% enabled switching between visualization modes. While nearly all dashboards showed time trends, only 6% provided zip code-level detail, and over a third lacked race and ethnicity data. Single external reviewer demonstrated strong correlation (r = 0.84, p = 0.002). Dashboard scores were not correlated with state population size (r = 0.187, p = 0.193).</p><p><strong>Conclusions: </strong>State opioid surveillance dashboards varied substantially in observable capabilities. This framework provides a standardized method to compare dashboard features and identify gaps that may inform future refinement.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148005186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in Technology-Assisted Visual Rehabilitation Therapies: Scoping Review.","authors":"Mariana Saiz Briceño, Maria Paula Trujillo López, Laura Valentina Botia Suárez, Juan Camilo Tobo Hernández, Erwin Hernando Hernández Rincón","doi":"10.1007/s10916-026-02410-4","DOIUrl":"10.1007/s10916-026-02410-4","url":null,"abstract":"<p><p>Visual impairment (VI) is a growing condition associated with aging, neurological diseases, and chronic eye conditions. In 2023, more than 2.2 billion people experienced VI, and at least 1 billion could have been prevented or remain untreated. Visual rehabilitation (VR) is essential for optimizing residual vision and promoting functional independence; however, only a minority have access to these services. In recent years, the development of technologies such as artificial intelligence (AI), mobile applications, and virtual reality (VRt) has transformed VR, offering accessible and personalized alternatives. This scoping review aimed to map and categorize the available evidence on technology-assisted visual rehabilitation therapies, identifying the types of technologies used, their applications, and reported clinical and functional outcomes. A scoping review was conducted following the Joanna Briggs Institute (JBI) methodology and PRISMA-ScR guidelines to map the literature published between 2015 and 2025 on technology-assisted VR therapies in adults with VI worldwide. Twenty-five studies, mainly from developed countries, were included. Interventions included videoconferencing, computerized systems, portable devices, VRt, artificial vision (AV), and smart sensors. Overall, digital technologies were shown to improve visual function, autonomy, and quality of life, with high patient satisfaction and adherence. Telerehabilitation (TR), understood as the remote delivery of visual rehabilitation services through videoconferencing and digital monitoring platforms, has emerged as a viable and cost-effective strategy. In contrast, immersive virtual reality technologies (VRt), head-mounted display systems, and multisensory integration devices have demonstrated potential to stimulate neuroplasticity and enhance functional visual outcomes.Telerehabilitation platforms, immersive virtual reality systems, portable electronic vision enhancement devices, artificial vision technologies, and sensor-based monitoring tools are emerging as promising interventions. These technologies have demonstrated improvements in visual acuity, reading performance, visual field sensitivity, patient adherence, and functional autonomy, although most studies remain small-scale and geographically concentrated in high-income settings.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13197244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147987421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RetCond: A Conditional Diffusion Model for Self-Explanatory Multi-Class Fundus Image Classification.","authors":"Ahmad O Ahsan, Christopher Nielsen, Raissa Souza, Agampreet Aulakh, Nils D Forkert, Matthias Wilms","doi":"10.1007/s10916-026-02414-0","DOIUrl":"10.1007/s10916-026-02414-0","url":null,"abstract":"<p><p>Vision impairment remains a major global health challenge, and early diagnosis of retinal diseases is essential to prevent avoidable vision loss. Color fundus photography is widely used for retinal assessment, but manual interpretation is time-consuming and prone to inter-clinician variability. Although deep learning has improved automated retinal disease detection, most classification models lack transparency. We introduce RetCond, a self-explanatory generative classifier designed to provide both accurate predictions and counterfactual visual explanations. RetCond is a diffusion model repurposed as a classifier whose generative capabilities enable counterfactual synthesis for built-in explanations. These counterfactual images depict the retinal changes associated with alternative diagnoses, offering insight into the model's decisions. We curated a diverse dataset of 19,565 images containing five retinal disease conditions to train and evaluate the model. Performance is assessed using standard classification metrics and by qualitatively and quantitatively evaluating the self-explanatory component. Comparisons are made against strong discriminative baselines. RetCond achieves a classification accuracy of 96.98%, comparable to RETFound (96.99%). In contrast to standard models, RetCond produces counterfactual images that highlight condition-specific retinal features, confirming its learned concepts and demonstrating its self-explanatory properties. These results show that RetCond matches state-of-the-art discriminative performance while addressing limitations of post-hoc interpretability techniques in standard deep learning models. RetCond, therefore, constitutes a contribution toward more trustworthy AI solutions in ophthalmology.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147987416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Motion-Based Confidence Score to Support the Practical Application of rPPG Methods in Health Monitoring.","authors":"Miguel Arevalillo-Herráez, Yuyan Wu, Benjamin Tilbury, Naeem Ramzan","doi":"10.1007/s10916-026-02412-2","DOIUrl":"10.1007/s10916-026-02412-2","url":null,"abstract":"<p><p>Remote photoplethysmography (rPPG) has gained popularity as a non-invasive technique for remote monitoring, as it can provide accurate measurements of an individual's physiological signals under controlled conditions. However, the accuracy of rPPG can be affected by various factors, such as movement artifacts, changes in skin tone, and the presence of other sources of light in the environment. To improve the reliability of rPPG measurements in real-world monitoring settings and reduce the frequency of false alarms in health monitoring settings, we propose a confidence score indicating the quality of the predictions. This score was built by identifying meaningful variables related to motion that strongly correlate with the accuracy of the measurements and training a classifier with data coming from 3 distinct datasets, to improve the model's robustness, reproducibility, and generalizability. Despite that only motion-related features have been considered, the high AUC values obtained in all cases were always above 0.93, demonstrating the model's ability to detect inaccurate heart rate measurements.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13183698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147963973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models for Semantic Interoperability in Value-Based Perioperative Care.","authors":"Seshadri C Mudumbai, Vikas N O'Reilly-Shah, Lichy Han, Brian S Rothman","doi":"10.1007/s10916-026-02404-2","DOIUrl":"10.1007/s10916-026-02404-2","url":null,"abstract":"<p><p>Proven protocols remain isolated inside institutions because EHR systems cannot understand each other's data semantically. Current data models each advance semantic interoperability, but none fully solves the problem of cross-institutional protocol portability. The authors suggest that large language models can translate data across systems using existing data model frameworks as \"clinical constitutions\" to achieve interoperability without universal standards.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147941599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VR-Based Physical Activity and Self-Management Interventions for Glycemic Control and Behavioral Outcomes in Type 2 Diabetes: A Systematic Review and Meta-Analysis.","authors":"Mengyuan Leng, Shiying Tang, Chenxin Gan, Jingjing Xu, Dan Luo","doi":"10.1007/s10916-026-02403-3","DOIUrl":"10.1007/s10916-026-02403-3","url":null,"abstract":"<p><p>A growing body of original research has recently investigated virtual reality (VR)-based interventions for physical activity and self-management, yet yielded inconsistent results. This systematic review and meta-analysis aims to evaluate the effectiveness of VR-based physical activity and self-management interventions in improving glycemic control and modifying health-related behaviors among patients with type 2 diabetes mellitus (T2DM). A total of 10 studies involving 1,005 adults with T2DM were included, comprising 5 randomized controlled trials (RCTs), 1 non-randomized controlled trial (nRCT), and 4 self-controlled before-after studies. Of these, participants in the intervention groups received VR-based interventions, including immersive or interactive VR programs for diabetes self-management education or physical activity promotion, with most intervention durations ranging from 8 to 24 weeks. Control groups received traditional approaches, including routine care, in-person diabetes self-management programs, and content-equivalent 2D websites. The pre-post intervention analysis revealed that glycated hemoglobin (HbA1c) levels decreased significantly post-intervention (mean difference (MD) = -0.50, 95% CI: -0.92 to -0.08, p = 0.02). While VR-based physical activity and self-management interventions did not demonstrate superior efficacy to traditional intervention methods in glycemic control or behavioral outcomes, their unique advantages - including enhanced adherence, greater accessibility, and reduced spatiotemporal constraints - position them as a viable alternative for diabetes management. Nevertheless, the field requires more rigorously designed RCTs to establish efficacy and a systematic exploration of VR implementation protocols to maximize therapeutic benefits.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147930451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When Chatbots Become Agents: The Next Phase of Healthcare AI.","authors":"Nalan Karunanayake","doi":"10.1007/s10916-026-02402-4","DOIUrl":"10.1007/s10916-026-02402-4","url":null,"abstract":"<p><p>Healthcare artificial intelligence (AI) has moved beyond answering medical questions. In early 2026, OpenAI, Anthropic, and Google launched agentic systems that retrieve evidence, use tools, and execute multi-step workflows. These systems can integrate information across multiple clinical knowledge domains within a single workflow, but the evidence base for evaluating their reliability in clinical practice has not kept pace with deployment. This perspective argues that healthcare is adopting a technology evolving faster than anyone can evaluate, and that a brief window exists to shape integration before the architecture becomes entrenched. These systems share common architectural principles, yet only 19 prospective trials exist among over 4,600 clinical large language model studies, none evaluating agentic pipelines. The architecture is locking in, professional roles are being redefined, and builders hold a structural information advantage. Transparency, adaptive governance, and broader participation are needed before these choices become irreversible, and to ensure that the boundary between human clinical judgment and automated capability is drawn in a way that strengthens both. Every stakeholder in healthcare must engage with this shift now.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147930383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Physical, Occupational and Neuro-Rehabilitation: Clinical Effectiveness, Prognostic Performance, and Pre-Implementation Feasibility - A Systematic Review.","authors":"Rabie Adel El Arab, Omayama Abdulaziz Al Moosa, Wesam Taher Almagharbeh, Fuad Abuadas, Nafisa Abdalla, Amany Abdrbo, Salwa Hassanein","doi":"10.1007/s10916-026-02400-6","DOIUrl":"10.1007/s10916-026-02400-6","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Global rehabilitation needs far exceed capacity, and artificial intelligence (AI) is proposed to extend access, personalise therapy, and support adherence. We aimed to synthesise evidence on clinical effectiveness, prognostic performance, and implementation feasibility of AI-enabled rehabilitation across conditions and care settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a mixed-methods systematic review of AI-enabled rehabilitation and rehabilitation-led prevention relevant to physiotherapy practice. MEDLINE, Embase, Web of Science, CINAHL, Scopus, and IEEE Xplore were searched. Methodological quality was appraised with MMAT and PROBAST + AI (prediction/diagnostic models), with a priori AI-reporting minimums checklist. Given heterogeneity, evidence was integrated via prespecified thematic synthesis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;Thirty studies from diverse regions met inclusion, comprising randomised trials, non-randomised comparisons, cohorts, surveys, qualitative work, and prediction/diagnostic models. Clinical effects were modest and heterogeneous. Most AI-enabled interventions were comparable to conventional rehabilitation; signals of benefit appeared in selected musculoskeletal and telerehabilitation contexts but rarely persisted beyond short follow-up. Internal validity was frequently limited by asymmetric adherence measurement objective telemetry in AI arms versus self-report or attendance in controls alongside low uptake and declining engagement over time. Safety and usability were generally favourable within short horizons, although surveillance and explicit attribution to AI components were inconsistently reported. Prognostic and adaptive models showed encouraging discrimination in development settings but lacked multicentre external validation, calibration, subgroup-error profiling, and prospective impact evaluation, leaving them unready for clinical use. Stakeholders reported willingness to adopt AI while highlighting gaps in training, governance and information technology support, costs, and digital equity. Overall certainty for comparative clinical outcomes was low to moderate, for models, very low.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;AI in rehabilitation presently acts more as a behavioural amplifier structuring home programmes and supporting execution than as a replacement for dose-matched therapist-delivered care. Credible scale-up should position AI as an adjunct and hinge on arm-symmetric capture of adherence and execution, clinically meaningful outcomes paired with verified behaviour change over treatment and longer-term follow-up, and model deployment only after transparent development, external validation with calibration and subgroup-error characterisation, and demonstration of clinical impact. Embedding equity and economic evaluation, together with living oversight and version control, will be essential to convert promising prototypes into trustworthy, durable, and widely accessibl","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147874299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Plausibility in Large Language Model Robustness Testing for Medicine: A Scoping Review.","authors":"Yu Chang, Ming-Hong Hsieh, Po-Chung Ju, Yi-Chun Liu, Cheng-Chen Chang","doi":"10.1007/s10916-026-02405-1","DOIUrl":"10.1007/s10916-026-02405-1","url":null,"abstract":"<p><p>Large language models (LLMs) show promise in medical applications, yet their translation into clinical practice requires rigorous validation. Current robustness testing often employs adversarial approaches borrowed from AI safety, raising questions about their alignment with authentic clinical scenarios. To systematically map methodologies used for robustness testing of LLMs in medical contexts and assess their clinical plausibility. A scoping review was conducted following PRISMA-ScR guidelines, searching PubMed, Embase, Web of Science, IEEE Xplore, ACM Digital Library, arXiv, and MedRxiv from January 2023 to September 2025. Two independent physician reviewers screened 5,331 articles, extracting data on testing methodologies, medical domains, expert involvement, and clinical plausibility. Thirty-three studies met inclusion criteria, predominantly from 2025 (82%). The most common robustness testing approaches were misleading prompts (49%) and adversarial prompts (39%). Only 33% of studies designed tests clearly mimicking plausible clinical scenarios. While 58% reported expert involvement, the depth of integration varied considerably. Studies predominantly addressed mixed medical domains (73%) rather than specialized fields. The emerging literature suggests that LLM robustness testing in medicine often emphasizes technical vulnerability detection, with fewer studies examining clinically plausible scenarios of routine use. Future frameworks should complement adversarial testing with clinically grounded, longitudinal, and specialty focused evaluations to support deployment-relevant inference.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13161009/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147868616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility, Adherence, Acceptance and Usability of a Multimodal Telemonitoring for Pediatric Post-COVID Syndrome: A Bicentric Pilot Study.","authors":"Zoe S Oftring, Jeremy Schmidt, Julia Greenfield, Matthias Hägele, Armin Farzaneh, Eckard Hamelmann, Uta Behrends, Sebastian Kuhn","doi":"10.1007/s10916-026-02409-x","DOIUrl":"10.1007/s10916-026-02409-x","url":null,"abstract":"<p><p>Existing healthcare infrastructure struggles to meet the complex care required for pediatric Post-COVID Syndrome (pPCS). Telemonitoring offers potential to enhance care access, reduce patient burden, and ensure continuity. This study introduces and evaluates a novel, multimodal telemonitoring concept for pPCS with high translational potential for broader pediatric chronic and post-infectious conditions. Telemonitoring included a patient app, digital sensors (spirometer, smartwatch), Patient Reported Outcome Measures, chat/video consultations (VC), and a medical telemonitoring platform. Patients aged 12-17 years with diagnosed PCS were recruited from two pPCS outpatient university clinics in Bielefeld and Munich, Germany. Monitoring lasted three months. Evaluation focused on feasibility, adherence, acceptance, and usability, using monitoring data, the System Usability Scale (SUS), Technology Usage Inventory (TUI), and a custom survey completed by patients and parents. 30 patients (mean age: 15y ± 1.9; 57% female (17/30); mean Baseline Bell-Score: 36.4) and 30 parents participated. Adherence was high, with an average of 3.4 (smartwatch) to 4.6 (spirometry) measurements/week. Questionnaire response rate was 86% (411/480) and 97% (58/60) of VCs were conducted. SUS scores indicated very high usability (patients: 81.25/100; parents: 75.42/100). TUI results showed low skepticism, and high interest. Telemonitoring supported symptom management independent of in-person visits, despite sensor connectivity issues. This is the first study to demonstrate successful integration of telemonitoring in pPCS, with high adherence and positive feedback from all stakeholders supporting its potential. Despite occasional technical challenges and resource needs, this concept shows promise for broader hybrid telemonitoring care implementation in PCS and other post-infectious syndromes. TRIAL REGISTRATION: German Clinical Trials Register (DRKS), trial registration number: DRKS00029354. Registered 07 February 2023 - Retrospectively registered https://drks.de/search/en/trial/DRKS00029354/entails.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13157441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147864252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}