European heart journal. Digital health最新文献

{"title":"Time-structured detection of pre-event cardiovascular instability using CPAP-derived Cheyne-Stokes breathing.","authors":"Kimimasa Saito, Yoko Takamatsu","doi":"10.1093/ehjdh/ztag108","DOIUrl":"https://doi.org/10.1093/ehjdh/ztag108","url":null,"abstract":"<p><strong>Aims: </strong>Continuous positive airway pressure (CPAP) telemonitoring provides daily respiratory metrics, including Cheyne-Stokes breathing percentage (CSB%), which may reflect ventilatory-circulatory instability. However, inter-individual variability limits the clinical applicability of fixed thresholds. We developed a time-structured detection architecture using CPAP telemonitoring time-series data and evaluated its ability to identify temporal patterns preceding cardiovascular and cerebrovascular events.</p><p><strong>Methods and results: </strong>In this retrospective observational study, 1265 patients with obstructive sleep apnoea undergoing CPAP telemonitoring were analysed. Daily CSB% values were smoothed using a 3-day moving average and evaluated relative to individualized dynamic baselines. The detection framework consisted of two complementary components: identification of sustained deviation from baseline and detection of abrupt pre-event surges. Central apnoea predominance, assessed using the central apnoea index, was incorporated as a hierarchical escalation layer. Among 25 adjudicated cardiovascular and cerebrovascular events in 20 patients (heart failure 11, atrial fibrillation 7, cerebrovascular accident 7), the architecture detected 23 events (92.0%) within the predefined D-28 window. Median lead time was 12.0 days (IQR 2.8-24.0). False-positive alerts were concentrated within a subset of individuals. Hierarchical filtering reduced alerts by 94.9% relative to the baseline signal layer while preserving event enrichment. Distinct temporal phenotypes, including trajectory-dominant and spike-dominant patterns, were observed across disease categories, consistent with disease-specific pre-event dynamics.</p><p><strong>Conclusion: </strong>CPAP-derived CSB% may function as a time-structured digital biomarker reflecting evolving ventilatory-circulatory instability. A trajectory-based detection architecture may enable early identification of cardiovascular instability while maintaining operational feasibility in large-scale telemonitoring environments.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag108"},"PeriodicalIF":4.4,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13475116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148766513","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":"Performance metrics and reference standard uncertainty in the CT-LVEF study.","authors":"Emaan Fatima","doi":"10.1093/ehjdh/ztag106","DOIUrl":"10.1093/ehjdh/ztag106","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 6","pages":"ztag106"},"PeriodicalIF":4.4,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13372028/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457651","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 therapeutics in peripheral artery disease: a step forward, not yet across the finish line.","authors":"Christos Rammos, Julia Lortz","doi":"10.1093/ehjdh/ztag084","DOIUrl":"10.1093/ehjdh/ztag084","url":null,"abstract":"<p><p>Graphical AbstractFor image description, please refer to the figure legend and surrounding text.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 6","pages":"ztag084"},"PeriodicalIF":4.4,"publicationDate":"2026-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13310012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148347597","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":"Prediction of appropriate implantable cardioverter-defibrillator therapy using machine learning and routinely available clinical data.","authors":"Toshinori Chiba, Serafina Wegner, Emanuel Heil, Verena Tscholl, Robert Haettasch, Patrick Nagel, Johannes Lucas, Nikolaos Dagres, Felix Balzer, Alexander Meyer, Wilhelm Haverkamp, Florian Blaschke, Gerhard Hindricks, Felix Hohendanner","doi":"10.1093/ehjdh/ztag102","DOIUrl":"10.1093/ehjdh/ztag102","url":null,"abstract":"<p><strong>Aims: </strong>Risk stratification for appropriate implantable cardioverter-defibrillator (ICD) therapy remains imprecise when based on conventional clinical variables alone. We aimed to develop and geographically validate a machine-learning model that integrates routinely available clinical, electrocardiogram, and device interrogation/programming parameters, and quantify the incremental value of non-sustained ventricular tachycardia (NSVT).</p><p><strong>Methods and results: </strong>We retrospectively analysed 514 ICD recipients implanted between 2020 and 2025 at two hospital sites (development cohort) and an independent cohort of 220 patients from a third site (external validation). The endpoint was appropriate ICD therapy (anti-tachycardia pacing or shock). Models were trained using nested stratified cross-validation in the development cohort, and the final refitted model was applied to the external cohort without recalibration. In the development cohort, 77/514 patients (15%) experienced appropriate ICD therapy (follow-up 404 days). A base model (histogram-based gradient boosting) excluding NSVT achieved modest discrimination [receiver operating characteristic (ROC)-area under the curve (AUC) 0.624; average precision 0.198]. Adding NSVT improved performance (ROC-AUC 0.805; average precision 0.432). Logistic regression with NSVT achieved comparable discrimination (ROC-AUC 0.78; average precision 0.43), indicating a largely NSVT-driven gain. External validation (event rate 51/220, 23%) confirmed good discrimination (ROC-AUC 0.815; average precision 0.583) with acceptable calibration (Brier score 0.137). At the pre-specified threshold of 0.16 in external validation, sensitivity was 0.78 and specificity was 0.74.</p><p><strong>Conclusion: </strong>A machine-learning model integrating routinely available clinical, electrocardiogram, and ICD programming/interrogation data may enable the prediction of appropriate ICD therapy with preserved performance on geographic external validation. NSVT was the dominant contributor to predictive performance across modelling approaches.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 6","pages":"ztag102"},"PeriodicalIF":4.4,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13344847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413842","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 analysis of single-lead electrocardiograms enables pragmatic heart failure risk assessment in the general population.","authors":"Meraj Neyazi, Jan P Bremer, Jan Brederecke, Marius S Knorr, Ferdinand Seum, Carla Reinbold, Stefan Gross, Dora Csengeri, Stefan Blankenberg, Marcus Dörr, Marcus Vollmer, Renate Bonin-Schnabel","doi":"10.1093/ehjdh/ztag099","DOIUrl":"10.1093/ehjdh/ztag099","url":null,"abstract":"<p><strong>Aims: </strong>Effective heart failure (HF) prevention requires early identification of high-risk individuals, yet population-wide stratification remains difficult. We evaluated whether deep learning using single-lead (lead I) electrocardiograms (ECGs), obtainable from medical systems and wearables, enables population-scale risk assessment. We developed AI-HF to estimate incident clinical HF risk using UK Biobank (UKB) data, validating in the prospective SHIP-START and SHIP-TREND cohorts.</p><p><strong>Methods and results: </strong>The analysis included 31 740 UKB participants (median age 64, 5.2 year follow-up, 243 events), 3025 SHIP-START participants (age 50, 15 year follow-up, 166 events), and 1342 SHIP-TREND participants (age 51, 9 year follow-up, 84 events). Participants with prevalent HF were excluded. Performance was evaluated at a harmonized 5-year prediction horizon. C-indices for incident clinical HF were 0.693 [95% confidence interval (CI) 0.654-0.732] in UKB, 0.715 (0.652-0.777) in SHIP-START, and 0.791 (0.749-0.833) in SHIP-TREND. Hazard ratios per standard deviation increase in AI-HF output were 1.67 (1.56-1.79), 1.43 (1.25-1.65), and 1.46 (1.34-1.59), respectively (all <i>P</i> < 0.001). Adding biometric variables improved discrimination modestly (C-indices: 0.714, 0.718, and 0.77).</p><p><strong>Conclusion: </strong>Across cohorts, AI-HF identified individuals at elevated 5-year incident clinical HF risk using single-lead ECGs. Given the ubiquity of wearables, this method may enable population-scale assessment to support targeted prevention and early intervention.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 6","pages":"ztag099"},"PeriodicalIF":4.4,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13326632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148378341","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":"Generating evidence using electronic alerts during routine care: a fully automated randomized controlled trial of oral fluid restriction in acute heart failure (THIRST alert trial).","authors":"Yang Chen, Daniel Higgins, Yogini Jani, Nausheen Saleem, Kris Chafer, Ewan McFarlane, Timothy Roberts, Steve Harris, Bryan Williams, Matthew R Sydes, Folkert W Asselbergs, Anoop D Shah, R Thomas Lumbers","doi":"10.1093/ehjdh/ztag098","DOIUrl":"10.1093/ehjdh/ztag098","url":null,"abstract":"<p><strong>Aims: </strong>Many medical treatments lack robust evidence of safety and effectiveness from randomized controlled trials (RCTs). This is partly due to the cost and complexity of performing traditional RCTs and because randomization is not routinely embedded in clinical care. We aimed to evaluate the feasibility of conducting a pragmatic RCT fully integrated into the electronic health record (EHR) system to streamline patient identification, randomization, treatment allocation, and outcome assessment in patients admitted with acute heart failure.</p><p><strong>Methods and results: </strong>THIRST Alert was a single-centre parallel-group, open-label, feasibility RCT embedded in a hospital EHR system from May 3 to 1 November 2023. Adult patients who received more than one dose of intravenous furosemide within 48 h of admission were eligible. An interruptive alert was triggered when physicians accessed the medication order chart of eligible patients, inviting them to enrol the patient in the study. Enrolled patients were randomized to either 'oral fluid restriction of 1 L per day' or 'no fluid restriction'. The co-primary feasibility outcomes were the total number of patients recruited and the documented difference in oral fluid intake within 48 h of randomization. Twenty-three patients (16%) were enrolled from 145 eligible patient admissions; there were no repeat admissions among enrolled patients. A total of 1191 enrolment alerts were triggered, reaching 216 individual clinicians. 22/23 trial participants (96%) had a diagnosis of heart failure. No significant difference in oral fluid intake was observed between the treatment groups (median difference 518 mL; 95% confidence interval: -235 to 1270; <i>P</i> = 0.18). Documented oral intake was numerically higher in the oral fluid restriction group (1168 mL; interquartile range [IQR] 932-1620 mL) compared to the group without restriction (650 mL; IQR 75-1102 mL), which may reflect documentation bias rather than a true increase in oral fluid intake or a lack of intervention efficacy.</p><p><strong>Conclusion: </strong>This study demonstrates the feasibility of a pragmatic RCT fully integrated within a hospital EHR system in acute care. Although alert-to-enrolment rate was modest, the overall recruitment rate was comparable to conventional acute care RCTs, highlighting the potential of EHR-embedded trials to efficiently address evidence gaps in the management of conditions such as acute heart failure.</p><p><strong>Clinicaltrialsgov: </strong>NCT05869656.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 6","pages":"ztag098"},"PeriodicalIF":4.4,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13335805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148439116","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 and the evolution of the electrocardiogram: from cardiovascular diagnostic tool to digital biomarker.","authors":"Ryan B Choi, Rohan Khera","doi":"10.1093/ehjdh/ztag091","DOIUrl":"10.1093/ehjdh/ztag091","url":null,"abstract":"<p><p>The electrocardiogram (ECG) is a cornerstone of cardiovascular care. Traditionally, it has relied on expert visual interpretation and rule-based systems to define the presence of disease. However, the integration of artificial intelligence (AI) has transformed the ECG into a high-dimensional biomarker capable of detecting signatures of both overt and subclinical disease. This review explores the historical progress of the technology from its inception to its diverse range of AI applications in the clinic and in research. We examine fundamental methodological advancements, including a range of deep learning methods, and the use of ECG images and wearable and portable devices for scaling these innovations globally. We also provide the full spectrum of AI-enabled care via applications for electrocardiograms, including (i) assistance to clinicians to perform interpretation of ECGs, (ii) augmenting their ability to detect latent signatures of disease from ECG, and (iii) prognostic and predictive applications of AI-ECG in cardiovascular care. Finally, we address critical challenges regarding model transparency, phenotypic selectivity, and the gap in the development of AI-ECG applications and their actual implementation. To realize the full potential of AI for ECGs, the field needs to evolve from singular AI-ECG tools evaluated in retrospective studies toward robust foundation models with broader multimodal integration and evaluation in rigorously performed randomized clinical trials. By unlocking latent physiological data, AI-ECG serves as a scalable engine for cardiovascular precision care.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 7","pages":"ztag091"},"PeriodicalIF":4.4,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13419341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148622910","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":"Pharmacist-led, digitally enabled transitional care after acute myocardial infarction: design and baseline characteristics of the AMI-HOPE nationwide stepped-wedge trial.","authors":"Melody Wang, Zhi Zhen Lim, Siew-Pang Chan, Zhen-Long Teo, Sock-Hwee Tan, Vinay Panday, Ho Jien Sze, Yee-May Wong, Grace Chang, Faclin Ng, Imran Syed, Patrick Lim, Zijuan Huang, Ziliang Lim, Valerie Teo, Ian Phoon, Galih Kunarso, Siang-Chew Chai, Kai-Rong Siau, Yew-Seng Kwan, Samuel Ho, Laurane Lim, Marvin Sim, Hui-Rei Yap, Wan-Lin Oh, Hui-Ping Chong, Angela Yeo, Praveen Deorani, Robert Morris, Hee-Hwa Ho, Derek J Hausenloy, Hwee-Lin Wee, Doreen Tan, Mark Y Chan","doi":"10.1093/ehjdh/ztag094","DOIUrl":"10.1093/ehjdh/ztag094","url":null,"abstract":"<p><strong>Aims: </strong>To describe the design of AMI-HOPE, a pharmacist-led, digitally enabled transitional care intervention for patients after acute myocardial infarction (AMI), and its nationwide stepped-wedge cluster-randomized evaluation across Singapore's public healthcare system.</p><p><strong>Methods and results: </strong>AMI-HOPE is designed as a pragmatic, open-label trial across three health clusters (7 hospitals, 18 polyclinics) in which hospitals crossed over from standard cardiologist-led care to intervention at 3 month intervals. We utilize the Health Discovery+ (HD+) platform for remote vital signs monitoring, rule-based alerts, pharmacist-led teleconsultations, and automated patient education. Target N = 1512 (1:1 ratio); primary outcome is a hierarchical win ratio composite (CV death, MI, stroke, CV readmissions, low-density lipoprotein cholesterol, systolic blood pressure, HbA1c, patient-reported outcome measures). Secondary outcomes include guideline-directed medical therapy titration/adherence, LVEF, and cost-effectiveness. Enrolment completed in September 2025 (N = 1634 randomized; 810 control, 824 intervention). Baseline characteristics (N = 1492 in follow-up) showed a median age of 59 years, 12% female, 61% Chinese ethnicity. Twelve-month follow-up will complete on 30 September 2026.</p><p><strong>Conclusion: </strong>AMI-HOPE demonstrates a scalable, digitally enabled model shifting early post-AMI care from specialist-led clinics to pharmacists with remote monitoring. This nationwide trial evaluates its clinical efficacy, implementation, and value at population scale. ClinicalTrials.gov: NCT07443982.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 6","pages":"ztag094"},"PeriodicalIF":4.4,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398992/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586192","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":"Fully automated artificial intelligence-based echocardiographic analysis for global longitudinal strain monitoring and cancer therapy-related cardiac dysfunction detection in breast cancer patients.","authors":"Yoshihito Saijo, Robert Zheng, Yuichiro Okushi, Yuka Nomura, Yukina Hirata, Hiroaki Inoue, Hirotsugu Yamada, Kenya Kusunose, Masataka Sata","doi":"10.1093/ehjdh/ztag097","DOIUrl":"10.1093/ehjdh/ztag097","url":null,"abstract":"<p><strong>Aims: </strong>Global longitudinal strain (GLS) is essential for the early detection of cancer therapy-related cardiac dysfunction (CTRCD). A fully automated echocardiographic analysis system using artificial intelligence (AI) may improve workflow efficiency in cardio-oncology. We sought to evaluate the feasibility and diagnostic performance of a fully automated AI-based echocardiographic system in breast cancer patients receiving cardiotoxic chemotherapy.</p><p><strong>Methods and results: </strong>In this prospective observational study, patients with breast cancer undergoing anthracyclines and/or HER2-targeted therapy between January 2022 and June 2025 were enrolled. Transthoracic echocardiography was performed at baseline and every 12 weeks. GLS was measured manually by two experts and automatically by a fully automated AI-based analysis system. A total of 92 patients (456 echocardiographic studies) were analysed. AI-derived GLS values were significantly lower than expert measurements (17.7 ± 2.9% vs. 18.4 ± 2.8%, <i>P</i> = 0.007). Correlation and agreement between the two methods were moderate (<i>R</i> = 0.64, intraclass correlation coefficient = 0.63). On linear mixed-effects modelling, longitudinal changes in GLS were not significantly different between methods (<i>P</i> = 0.72). GLS-based CTRCD was detected in 31.5% of patients by experts and 34.8% by AI (<i>P</i> = 0.58), with similar detection timing (<i>P</i> = 0.47). Diagnostic agreement was substantial (κ = 0.68, <i>P</i> < 0.001).</p><p><strong>Conclusion: </strong>The fully automated AI-based echocardiographic system demonstrated acceptable agreement and diagnostic performance for GLS assessment and showed a similar ability to track temporal relative GLS changes and identify CTRCD. However, systemic underestimation of absolute GLS values may contribute to threshold-based classification discordance in borderline cases.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 6","pages":"ztag097"},"PeriodicalIF":4.4,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13322393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148371144","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 stupidity or logimorphism? How misuse of language warps our thinking about 'artificial intelligence'.","authors":"Alan G Fraser","doi":"10.1093/ehjdh/ztag095","DOIUrl":"10.1093/ehjdh/ztag095","url":null,"abstract":"<p><p>'Artificial intelligence' (AI), as a blanket term, covers many advanced computing techniques that employ divergent methodologies for a wide array of functions. It is too late to affect general usage of the term without qualification-although it is a misnomer-but perhaps not too late to argue for the preferential use of more specific and realistic terminologies when discussing useful applications in science and medicine. Large language models are statistical tools for predicting text; machine learning algorithms are programs that discern patterns from data; and neural networks are mathematical models that predict outputs from inputs. In all cases, the software is unaware of what it is doing or why. The real intelligence is human, exemplified by the expertize of the engineers who designed any particular system, and by the scepticism, realism and vision of those who interpret and apply its outputs. Users may suspend their incredulity if large language models that are not sentient creatures are programmed to answer questions in the first person, since that encourages anthropomorphism. Perhaps we need a new word-which could be '<i>logimorphism'</i>-to emphasize the dangers of interacting uncritically with inanimate software programs while overtly or subconsciously ascribing them human cognitive powers. Implementing high standards when applying new computing tools in clinical research and practice should start with the avoidance of inappropriate language that degrades our thinking.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"7 6","pages":"ztag095"},"PeriodicalIF":4.4,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13326636/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148378278","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}