European heart journal. Digital health最新文献

{"title":"Comparative evaluation of artificial intelligence-assisted literature search tools for identifying clinically meaningful evidence in cardiology.","authors":"Riccardo Di Febo, Maximiliano Jeanneret Medina, Alexandre Renaud, Maryam Dridi, Valentine Pecriaux, Benoit Lequeux, Stephane Lafitte, Baptiste Maille, Aymeric Menet","doi":"10.1093/ehjdh/ztag125","DOIUrl":"10.1093/ehjdh/ztag125","url":null,"abstract":"<p><strong>Aims: </strong>The rapid expansion of biomedical literature challenges clinicians' and researchers' ability to identify clinically meaningful evidence. We systematically compared five literature search tools, four artificial intelligence (AI)-assisted and one conventional, across clinically relevant cardiology research scenarios, using a blinded expert-validated gold standard to assess their ability to retrieve relevant and key references.</p><p><strong>Methods and results: </strong>We evaluated ChatGPT-5, Elicit, Consensus, Scite, and PubMed across four cardiology topics defined by maturity and specificity, with multiple standardized prompts. Three electrophysiology experts independently and blindly rated all retrieved references, defining two gold standards: expert-rated relevance and expert-selected key references. ChatGPT-5 achieved the highest proportion of relevant articles (90% [88-100], <i>P</i> < 0.001) and the highest key-reference overlap (60% [43-68], <i>P</i> < 0.001), whereas Scite performed lowest (20% and 10%, respectively). The tool was the primary determinant of performance (partial <i>R</i> ² = 0.50), whereas prompt formulation had no significant effect. In a pre-specified subanalysis restricted to clinical studies, ChatGPT-5 and human-conducted systematic reviews overlapped by 42% (96% of shared articles highly relevant), with 58% distinct references, indicating complementary AI and human retrieval; ChatGPT-5 produced hallucinated citations when long reference lists were requested for emerging topics, underscoring the need for human verification.</p><p><strong>Conclusion: </strong>AI-assisted tools showed heterogeneous performance, ChatGPT-5 performing best in this cardiology setting. These preliminary, context-specific findings support hybrid human-AI strategies in which AI complements rather than replaces transparent database searches such as PubMed; larger-scale, multi-domain studies are needed to confirm and generalize them.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag125"},"PeriodicalIF":4.4,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13520940/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842134","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":"Disease-focused evaluation of AI-ECG tools: clarifying study design, findings, and the principles of open science.","authors":"Gamze Babur Guler, Arda Guler, Ibrahim Halil Tanboga","doi":"10.1093/ehjdh/ztag120","DOIUrl":"10.1093/ehjdh/ztag120","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag120"},"PeriodicalIF":4.4,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13436683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674472","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":"How should patient knowledge trigger clinical action in artificial intelligence-enabled pulmonary hypertension care?","authors":"Ilma Nascimento, Caio Julio Cesar Dos Santos Fernandes","doi":"10.1093/ehjdh/ztag123","DOIUrl":"10.1093/ehjdh/ztag123","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag123"},"PeriodicalIF":4.4,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13436678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674491","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":"Correction to: A machine learning model to detect falls mimicking cardiac arrest-related collapse based on wrist-derived accelerometry: the DETECT-2 study.","authors":"","doi":"10.1093/ehjdh/ztag116","DOIUrl":"https://doi.org/10.1093/ehjdh/ztag116","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1093/ehjdh/ztag043.].</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 6","pages":"ztag116"},"PeriodicalIF":4.4,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13399155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586151","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":"The impact of digital health on hospitalization and mortality in heart failure patients in low- and middle-income countries: a systematic review and meta-analysis.","authors":"Preston Nicely, Emily Eichstaedt, Charles Heald, Jack Simmons, Shiv Ishpujani, Stephen Clarkson","doi":"10.1093/ehjdh/ztag117","DOIUrl":"10.1093/ehjdh/ztag117","url":null,"abstract":"<p><strong>Aim: </strong>Heart disease is the leading cause of death worldwide, and the growing prevalence of heart failure (HF) is a key contributor. Digital health (dHealth) modalities including mobile applications, telehealth, and wearables have been developed to improve HF care, though little is known about their impact in low- and middle-income countries (LMICs). The aim of this review is to identify studies from LMICs that evaluated dHealth interventions for HF and determine the impact of those interventions on hospitalization and mortality.</p><p><strong>Methods and results: </strong>Six databases were searched from 1 January 1990 to 11 June 2025. A total of 3524 articles were screened (Cohen's κ = 69%). Included studies had a dHealth intervention, were a randomized controlled trial (RCT), occurred in a LMIC, and reported outcomes of interest. Risk of bias for RCTs was assessed using the Cochrane Risk of Bias 2 Tool. Fifteen RCTs (<i>n</i> = 4568) with studies in Brazil (33.3%), China (26.7%), and Thailand (13.3%) were included. Interventions included telemonitoring, mobile apps, and wearables. The population averaged 61.1 years, 31.4% female, and 45.7% New York Heart Association class III-IV. Random-effects meta-analysis showed dHealth reduced all-cause hospitalizations [risk ratio (RR) 0.73, 95% confidence interval (CI) 0.59-0.90], HF hospitalizations (RR 0.70, 95% CI 0.59-0.84), and all-cause mortality (RR 0.86, 95% CI 0.79-1.00); HF mortality was neutral (RR 0.64, 95% CI 0.37-1.11).</p><p><strong>Conclusion: </strong>Digital health in LMICs lowers hospitalizations and may reduce all-cause mortality, though the benefit for HF mortality remains uncertain. These findings support broader dHealth adoption in LMICs to address HF burden.PROSPERO registration number: CRD420251079715.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag117"},"PeriodicalIF":4.4,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13447851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148690061","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":"Large language model-based simulated patient training for heart failure palliative care communication: a pilot study.","authors":"Risa Kishikawa, Hiroyuki Morita, Satoshi Kodera","doi":"10.1093/ehjdh/ztag119","DOIUrl":"10.1093/ehjdh/ztag119","url":null,"abstract":"<p><strong>Aims: </strong>Heart failure (HF) palliative care communication is essential but difficult to train at scale because conventional role-play programmes require facilitators and standardized patients. Large language models (LLMs) have emerged as potential tools for scalable communication training. This pilot study aimed to evaluate the feasibility of a web-based LLM-driven communication training application and to explore its early educational signal on physicians' self-efficacy.</p><p><strong>Methods and results: </strong>This single-arm pilot study included physicians who completed one session using a Japanese-language web-based LLM application designed to simulate patients with advanced HF and provide automated framework-based feedback. The primary outcome was change in self-efficacy scores assessed by pre- and post-session questionnaires. Ten sessions were analysed. Physicians engaged in a mean of 7.6 ± 2.0 dialogue turns. Mean response time per model output and feedback generation were approximately 3 and 17 s, respectively. Significant improvements were observed in knowledge of palliative care communication (mean difference +1.7, adjusted <i>P</i> < 0.01) and confidence in HF palliative care communication (+1.2, adjusted <i>P</i> = 0.03). Other domains showed non-significant changes.</p><p><strong>Conclusion: </strong>This pilot study demonstrated the feasibility of a web-based LLM-simulated patient system and suggested an early educational signal in physicians' self-efficacy for HF palliative care communication. Our scalable LLM-driven communication training may complement traditional educational approaches with further evaluation in larger controlled studies.</p><p><strong>Clinical trial registration: </strong>Trial registration number: UMIN000059988.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag119"},"PeriodicalIF":4.4,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13436688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674660","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":"Artificial intelligence-enhanced electrocardiography for the prediction of future type 2 diabetes mellitus: a model-development and multicentre validation study.","authors":"Libor Pastika, Konstantinos Patlatzoglou, Ewa Sieliwonczyk, Joseph Barker, Boroumand Zeidaabadi, Kathryn A McGurk, Sandhi M Barreto, Lidyane Camelo, Sadia Khan, William R Scott, Declan P O'Regan, Bruce B Duncan, Maria I Schmidt, James S Ware, Shivani Misra, Daniel B Kramer, Jonathan W Waks, Nicholas S Peters, Antonio Luiz Pinho Ribeiro, Arunashis Sau, Fu Siong Ng","doi":"10.1093/ehjdh/ztag118","DOIUrl":"10.1093/ehjdh/ztag118","url":null,"abstract":"<p><strong>Aims: </strong>A significant proportion of type 2 diabetes cases remain undiagnosed despite screening advances, carrying substantial cardiometabolic risk. Artificial intelligence-enhanced electrocardiography (AI-ECG) detects subtle ECG changes in subclinical disease, potentially enabling opportunistic screening.</p><p><strong>Methods and results: </strong>We developed AI-ECG Risk Estimator for Diabetes Mellitus (AIRE-DM), a convolutional neural network with discrete-time survival loss, for diagnosis of prevalent and prediction of incident type 2 diabetes. It was trained on 1 163 401 ECGs from 189 537 individuals from Beth Israel Deaconess Medical Center (BIDMC) and externally validated in UK Biobank (UKB; <i>n</i> = 65 606) and ELSA-Brasil (<i>n</i> = 13 739). AI-ECG Risk Estimator for Diabetes Mellitus demonstrated moderate discrimination for prevalent type 2 diabetes (area under the receiver operating characteristic curve: BIDMC 0.724, UKB 0.733, ELSA-Brasil 0.706) and incident type 2 diabetes (C-index: BIDMC 0.667, UKB 0.688, ELSA-Brasil 0.625). The highest AIRE-DM risk quartile had elevated incident diabetes risk vs. the lowest (hazard ratio: BIDMC 4.75, UKB 7.52, ELSA-Brasil 3.96). AI-ECG Risk Estimator for Diabetes Mellitus was non-inferior to the American Diabetes Association Diabetes Risk Test in BIDMC, with improved predictive accuracy when combined. In normoglycaemic patients, AIRE-DM was superior to glycated haemoglobin (HbA1c) for predicting incident diabetes in BIDMC and non-inferior in ELSA-Brasil. The highest risk quartile reached 5% cumulative type 2 diabetes mellitus incidence 5.4 years (BIDMC) and 4.8 years (ELSA-Brasil) earlier than the lowest risk quartile, after adjusting for HbA1c, age, and sex. Phenome- and genome-wide association studies revealed biologically plausible associations with glucose regulation, cardiac morphology, diastolic dysfunction, arterial stiffness, and lipid metabolism.</p><p><strong>Conclusion: </strong>AI-ECG Risk Estimator for Diabetes Mellitus detects prevalent type 2 diabetes and predicts incident disease, uniquely identifying high-risk individuals within the normoglycaemic range. Combined with clinical scores or biomarkers, it enhances risk stratification, enabling earlier intervention.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag118"},"PeriodicalIF":4.4,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13525370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148857918","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":"Rationale and design of the AutoHealth study: prospective clinical validation of continuous multimodal in-vehicle health monitoring in real-world mobility.","authors":"Dominik Naumann, Tatjana Amler, Doreen Schoeppenthau, Sergej Holzmann, Jörg Preißinger, Matthias Franz, Nils Hinrichs, Felix Schoenrath, Alexander Meyer","doi":"10.1093/ehjdh/ztag114","DOIUrl":"10.1093/ehjdh/ztag114","url":null,"abstract":"<p><strong>Aims: </strong>Continuous physiological monitoring outside clinical environments remains limited by usability, reproducibility, and user adherence. Vehicles offer a semi-controlled setting enabling unobtrusive multimodal sensing during everyday mobility. The Automotive Health Proof of Concept Trial (AutoHealth) investigates the real-world feasibility and accuracy of continuous in-vehicle cardiovascular monitoring using integrated optical, electrical, and acoustic sensors benchmarked against medical-grade reference standards.</p><p><strong>Methods and results: </strong>AutoHealth is a prospective, observational cohort study at Charité - Universitätsmedizin Berlin enrolling adults across five predefined cohorts: healthy individuals, patients with an elevated cardiometabolic risk, HFpEF, HFrEF, and persistent atrial fibrillation. Participants undergo comprehensive baseline phenotyping followed by a structured in-vehicle session comprising static and dynamic driving segments and predefined physical and cognitive stress tasks. Synchronized biosignals including rPPG, steering-wheel ECG, phonocardiography, and voice-derived features are compared with clinical reference measurements. The primary endpoint is the median absolute percentage error (MAPE) of in-vehicle vital sign estimates vs. reference measurements, with successful performance defined <i>a priori</i> as MAPE ≤10%. Secondary endpoints include the proportion of measurements meeting predefined clinical accuracy thresholds, arrhythmia detection performance, characterization of autonomic stress response, and correlations with functional mobility metrics. The study adheres to STROBE and SPIRIT-AI guidelines and is registered in the German Clinical Trials Register.</p><p><strong>Discussion: </strong>AutoHealth is designed to provide a prospective clinical validation of continuous multimodal cardiovascular monitoring inside a modified production vehicle under real-world driving conditions and reproducible closed-course testing. Study findings will characterize feasibility, performance, and translational potential of Automotive Health as a scalable prevention and remote physiological monitoring paradigm.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag114"},"PeriodicalIF":4.4,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13524371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148852060","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":"Deep learning-derived biological age from preoperative chest radiographs predicts mortality following surgical and transcatheter procedures for structural heart disease beyond EuroSCORE II.","authors":"Era Stambollxhiu, Miriam Kumpf, Maximilian-Niklas Bonk, Lisa C Adams, Marcus Makowski, Martin Hadamitzky, Markus Krane, Keno K Bressem, Oliver Deutsch","doi":"10.1093/ehjdh/ztag112","DOIUrl":"10.1093/ehjdh/ztag112","url":null,"abstract":"<p><strong>Aims: </strong>Accurate risk stratification before structural heart disease interventions is essential for clinical decision-making. Traditional risk models, such as the European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) and Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM), were designed for surgical patients and show inconsistent performance in transcatheter cohorts. Biological age, reflecting cumulative physiological decline, may offer prognostic value beyond chronological age and established risk scores.</p><p><strong>Methods and results: </strong>In this retrospective study of 1269 patients [non-transcatheter aortic valve implantation (TAVI) <i>n</i> = 751, TAVI <i>n</i> = 518] treated at the German Heart Center Munich, biological age was estimated from pre-operative chest radiographs using CXR-Age, a validated deep learning model. Analyses were conducted separately for surgical (non-TAVI) and transcatheter (TAVI) groups. For 30-day mortality, biological age outperformed EuroSCORE II in both subgroups [area under the receiver operating characteristic curve (AUC): non-TAVI 0.874 vs. 0.785, <i>P</i> < 0.001; TAVI 0.952 vs. 0.745, <i>P</i> = 0.004] and remained independently predictive after adjustment [TAVI OR 1.58 per year, 95% confidence interval (CI) 1.27-2.12]. While STS-PROM was the strongest single predictor for non-TAVI patients (AUC 0.949), it was similar to EuroSCORE II for TAVI patients (AUC 0.729). Notably, patients whose biological age exceeded their chronological age by more than 10 years faced higher major complication rates (17.3% vs. 9.2%; <i>P</i> = 0.016).</p><p><strong>Conclusion: </strong>Biological age distinguished risk across both populations, suggesting that deep learning-based biological age estimation from routine chest radiographs could serve as an automated, accessible complement to existing risk models.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag112"},"PeriodicalIF":4.4,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13436687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674495","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":"Digital biomarkers in cardiovascular medicine: a scientific statement of the ESC Working Group on e-Cardiology, the ESC Working Group on Cardiovascular Pharmacotherapy, the European Heart Rhythm Association (EHRA) of the ESC, the Association for Acute Cardiovascular Care (ACVC) of the ESC, the Heart Failure Association (HFA) of the ESC, the European Association of Preventive Cardiology (EAPC) of the ESC, the European Association of Cardiovascular Imaging (EACVI) of the ESC, in collaboration with the Digital Cardiology and Artificial Intelligence Committee of the ESC, and the ESC Regulatory Affairs Committee.","authors":"Pablo M Corredoira, Juan Carlos Kaski, David Duncker, Marcus Dörr, Matthias Wilhelm, Marco Tubaro, Mark J Schuuring, Mamas Mamas, Steffen E Petersen, Rafael Vidal-Pérez, Hareld M C Kemps, Emanuela T Locati, Christian Mueller, Magnus T Jensen, Raffaele De Lucia, Loreena Hill, Julia Ramírez, Matthijs Cluitmans, Nurgül Keser, Axel Verstrael, Polychronis E Dilaveris, Paul A Friedman, Rubén Casado-Arroyo","doi":"10.1093/ehjdh/ztag074","DOIUrl":"10.1093/ehjdh/ztag074","url":null,"abstract":"<p><p>Cardiovascular disease is the leading cause of morbidity and mortality globally, imposing a substantial patient burden. Digital biomarkers derived from digital health technologies enable quantifiable, near-continuous, real-world capture of pathophysiological signals and are increasingly used across cardiovascular medicine. Their growth is often supported by big data analytics, artificial intelligence and connected sensors within the Internet of Things. This scientific statement systematically reviews digital biomarkers, their clinical utility across the cardiovascular care continuum, and summarizes major methodological and implementation challenges. We searched PubMed/MEDLINE, Embase and Scopus for phase III-IV cardiovascular trials published between 1 January 2019 and 1 August 2024 that collected digital biomarkers. We identified 541 records and included 32 trials (40 reports) enrolling 32 246 participants (44.71% women; mean age 66.2 ± 11.0 years). Heart failure was the most frequent target condition (16 trials, 50%), followed by cardiac implantable electronic devices trials (12 trials, 38%). Digital biomarkers included cardiac rhythm and heart rate, blood pressure, body weight, impedance-based measures, haemodynamic pressures, and physical activity and sleep patterns captured using diverse sensor technologies. Methodological quality was good (modal quality score 5; range 3-7), although most trials were unblinded. Current studies suggest potential clinical utility in selected cardiovascular conditions. Implementation remains challenging because of measurement variability, privacy and regulatory requirements, affordability and reimbursement barriers and inequities in access. Robust validation and outcome- and cost-effectiveness evidence are still needed. Registration number: CRD42024573879.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 6","pages":"ztag074"},"PeriodicalIF":4.4,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13378772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148581029","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}