{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T05:27:02Z","timestamp":1788931622746,"version":"build-2803163510"},"reference-count":39,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Cardio"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Cardiovascular biomarkers are increasingly extracted or interpreted using artificial intelligence (AI) applied to electrocardiograms, imaging, laboratory measurements, electronic health records, wearable devices, and longitudinal data. Predictive performance alone, however, does not establish that a measurement is valid, that a model is transportable and calibrated, or that acting on its output improves care. Existing resources provide essential but complementary foundations: the US Food and Drug Administration\u2013National Institutes of Health (FDA-NIH) BEST (Biomarkers, EndpointS, and other Tools) resource standardizes biomarker terminology; the V3 and V3+ frameworks address verification, analytical validation, clinical validation, and usability of digitally measured signals; prediction-model and trustworthy-AI guidance addresses reporting, risk of bias, early clinical evaluation, and deployability; and regulatory qualification pathways evaluate evidence within a defined context of use. A remaining practical challenge is translating these complementary requirements into accountable clinical action within cardiovascular workflows. This viewpoint proposes a clinician-centered operational framework organized around validation, governance, and workflow integration. For each pillar, it identifies accountable actors, required steps, documented outputs, and escalation or stop rules. Validation establishes whether the input measurement and model are fit for the intended population and decision. Governance assigns institutional authorization, clinical ownership, version control, monitoring, and authority to restrict, pause, or withdraw the intervention. Workflow integration specifies who receives the output, what confirmatory action follows, how disagreement is handled, and how decisions are documented. A worked example of an AI-enabled electrocardiographic screening output for possible left ventricular systolic dysfunction illustrates the pathway from local evaluation to echocardiographic confirmation and lifecycle monitoring. The framework does not replace established validation or regulatory standards; it operationalizes them as a clinician-centered and institutionally accountable pathway from validated signal to governed, patient-facing action.<\/jats:p>","DOI":"10.2196\/103401","type":"journal-article","created":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T19:51:27Z","timestamp":1787946687000},"page":"e103401-e103401","source":"Crossref","is-referenced-by-count":1,"title":["Operationalizing AI-Enabled Cardiovascular Biomarkers: A Clinician-Centered Framework for Validation, Governance, and Workflow Integration"],"prefix":"10.2196","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8597-9198","authenticated-orcid":false,"given":"Dabeluchi Chiedozie","family":"Ngwu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,8,28]]},"reference":[{"issue":"3","key":"R1","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1067\/mcp.2001.113989","article-title":"Biomarkers and surrogate endpoints: preferred definitions and conceptual framework","volume":"69","author":"Biomarkers Definitions Working Group","journal-title":"Clin Pharmacol Ther"},{"issue":"8","key":"R2","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1161\/CIRCULATIONAHA.106.683110","article-title":"Benchmarks for the assessment of novel cardiovascular biomarkers","volume":"115","author":"Morrow","journal-title":"Circulation"},{"issue":"17","key":"R3","doi-asserted-by":"publisher","first-page":"2408","DOI":"10.1161\/CIRCULATIONAHA.109.192278","article-title":"Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association","volume":"119","author":"Hlatky","journal-title":"Circulation"},{"issue":"11","key":"R4","doi-asserted-by":"publisher","first-page":"e953","DOI":"10.1161\/CIR.0000000000001401","article-title":"Criteria to assess the predictive and clinical utility of novel models, biomarkers, and tools for risk of cardiovascular disease: a scientific statement from the American Heart Association","volume":"153","author":"Khan","journal-title":"Circulation"},{"issue":"2","key":"R5","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1038\/s41569-020-00445-9","article-title":"Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management","volume":"18","author":"Krittanawong","journal-title":"Nat Rev Cardiol"},{"issue":"10201","key":"R6","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/S0140-6736(19)31721-0","article-title":"An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction","volume":"394","author":"Attia","journal-title":"Lancet"},{"issue":"20","key":"R7","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1056\/NEJMoa1901183","article-title":"Large-scale assessment of a smartwatch to identify atrial fibrillation","volume":"381","author":"Perez","journal-title":"N Engl J Med"},{"issue":"9","key":"R8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacadv.2024.101183","article-title":"Transforming cardiovascular care with digital health: the past, progress, and promise","volume":"3","author":"Chaturvedi","journal-title":"JACC Adv"},{"key":"R9","doi-asserted-by":"publisher","DOI":"10.2196\/44983","article-title":"Digital transformation in the diagnostics and therapy of cardiovascular diseases: comprehensive literature review","volume":"7","author":"Stremmel","journal-title":"JMIR Cardio"},{"key":"R10","doi-asserted-by":"publisher","DOI":"10.2196\/73846","article-title":"AI-driven real-time monitoring of cardiovascular conditions with wearable devices: scoping review","volume":"13","author":"Abedi","journal-title":"JMIR Mhealth Uhealth"},{"issue":"2","key":"R11","doi-asserted-by":"publisher","first-page":"74","DOI":"10.14744\/AnatolJCardiol.2023.3685","article-title":"Artificial intelligence-based clinical decision support systems in cardiovascular diseases","volume":"28","author":"Bozyel","journal-title":"Anatol J Cardiol"},{"key":"R12","doi-asserted-by":"publisher","DOI":"10.2196\/52139","article-title":"Artificial intelligence-based electrocardiographic biomarker for outcome prediction in patients with acute heart failure: prospective cohort study","volume":"26","author":"Cho","journal-title":"J Med Internet Res"},{"key":"R13","unstructured":"FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource. Food and Drug Administration (US); National Institutes of Health (US); 2016. URL: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK326791 [Accessed 14-07-2026]"},{"key":"R14","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-0260-4","article-title":"Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)","volume":"3","author":"Goldsack","journal-title":"NPJ Digit Med"},{"issue":"1","key":"R15","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01322-2","article-title":"V3+ extends the V3 framework to ensure user-centricity and scalability of sensor-based digital health technologies","volume":"8","author":"Bakker","journal-title":"NPJ Digit Med"},{"key":"R16","doi-asserted-by":"crossref","unstructured":"Institute of Medicine (US) Committee on Qualification of Biomarkers and Surrogate Endpoints in Chronic Disease. Micheel CM Ball JR , editors. Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease. National Academies Press (US); 2010. [doi: 10.17226\/12869] [Medline: 25032382]","DOI":"10.17226\/12869"},{"issue":"5","key":"R17","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1373\/clinchem.2016.254649","article-title":"Waste, leaks, and failures in the biomarker pipeline","volume":"63","author":"Ioannidis","journal-title":"Clin Chem"},{"key":"R18","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.897106","article-title":"The translation and commercialisation of biomarkers for cardiovascular disease-a review","volume":"9","author":"Saleh","journal-title":"Front Cardiovasc Med"},{"issue":"10","key":"R19","doi-asserted-by":"publisher","DOI":"10.1056\/EVIDe2200222","article-title":"Clinical translation of cardiovascular biomarkers - a long and uncertain road","volume":"1","author":"Cunningham","journal-title":"NEJM Evid"},{"issue":"1","key":"R20","doi-asserted-by":"publisher","DOI":"10.1186\/s41512-019-0064-7","article-title":"A simple, step-by-step guide to interpreting decision curve analysis","volume":"3","author":"Vickers","journal-title":"Diagn Progn Res"},{"issue":"6","key":"R21","doi-asserted-by":"publisher","first-page":"H1752","DOI":"10.1152\/ajpheart.00024.2026","article-title":"Multiomics approaches to cardiovascular disease: technological innovations and clinical translation","volume":"330","author":"Zehra","journal-title":"Am J Physiol Heart Circ Physiol"},{"issue":"2","key":"R22","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1002\/sim.2929","article-title":"Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond","volume":"27","author":"Pencina","journal-title":"Stat Med"},{"key":"R23","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","journal-title":"BMJ"},{"key":"R24","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","journal-title":"BMJ"},{"key":"R25","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-081554","article-title":"FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare","volume":"388","author":"Lekadir","journal-title":"BMJ"},{"issue":"5","key":"R26","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","article-title":"Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","volume":"28","author":"Vasey","journal-title":"Nat Med"},{"key":"R27","unstructured":"Context of use. US Food and Drug Administration. Jan 21, 2026. URL: https:\/\/www.fda.gov\/drugs\/biomarker-qualification-program\/context-use [Accessed 14-07-2026]"},{"key":"R28","unstructured":"Qualification of novel methodologies for medicine development. European Medicines Agency. URL: https:\/\/www.ema.europa.eu\/en\/human-regulatory-overview\/research-development\/scientific-advice-protocol-assistance\/qualification-novel-methodologies-medicine-development [Accessed 14-07-2026]"},{"issue":"6464","key":"R29","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","journal-title":"Science"},{"issue":"12","key":"R30","doi-asserted-by":"publisher","first-page":"866","DOI":"10.7326\/M18-1990","article-title":"Ensuring fairness in machine learning to advance health equity","volume":"169","author":"Rajkomar","journal-title":"Ann Intern Med"},{"issue":"4","key":"R31","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1093\/ehjdh\/ztac065","article-title":"Importance of external validation and subgroup analysis of artificial intelligence in the detection of low ejection fraction from electrocardiograms","volume":"3","author":"Yagi","journal-title":"Eur Heart J Digit Health"},{"issue":"23","key":"R32","doi-asserted-by":"publisher","first-page":"e433","DOI":"10.1161\/CIR.0000000000001400","article-title":"Pragmatic approaches to the evaluation and monitoring of artificial intelligence in health care: a science advisory from the American Heart Association","volume":"152","author":"Jain","journal-title":"Circulation"},{"issue":"9","key":"R33","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1038\/s41591-019-0548-6","article-title":"Do no harm: a roadmap for responsible machine learning for health care","volume":"25","author":"Wiens","journal-title":"Nat Med"},{"key":"R34","volume":"2017","author":"Davis","journal-title":"AMIA Annu Symp Proc"},{"key":"R35","unstructured":"Regulation (EU) 2025\/327 of the European Parliament and of the Council of 11 February 2025 on the European Health Data Space and amending Directive 2011\/24\/EU and Regulation (EU) 2024\/2847. EUR-Lex. Feb 11, 2025. URL: http:\/\/data.europa.eu\/eli\/reg\/2025\/327\/oj [Accessed 31-05-2026]"},{"key":"R36","unstructured":"Regulation (EU) 2024\/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300\/2008, (EU) No 167\/2013, (EU) No 168\/2013, (EU) 2018\/858, (EU) 2018\/1139 and (EU) 2019\/2144 and Directives 2014\/90\/EU, (EU) 2016\/797 and (EU) 2020\/1828 (Artificial Intelligence Act). EUR-Lex. Jun 13, 2024. URL: http:\/\/data.europa.eu\/eli\/reg\/2024\/1689\/oj [Accessed 31-05-2026]"},{"key":"R37","unstructured":"Regulation (EU) 2017\/745 of the European Parliament and of the Council of 5 April 2017 on medical devices, amending Directive 2001\/83\/EC, Regulation (EC) No 178\/2002 and Regulation (EC) No 1223\/2009 and repealing Council Directives 90\/385\/EEC and 93\/42\/EEC. EUR-Lex. Apr 5, 2017. URL: http:\/\/data.europa.eu\/eli\/reg\/2017\/745\/oj [Accessed 31-05-2026]"},{"issue":"1","key":"R38","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1038\/s41591-018-0240-2","article-title":"Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram","volume":"25","author":"Attia","journal-title":"Nat Med"},{"issue":"5","key":"R39","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1038\/s41591-021-01335-4","article-title":"Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial","volume":"27","author":"Yao","journal-title":"Nat Med"}],"container-title":["JMIR Cardio"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T19:51:29Z","timestamp":1787946689000},"score":1,"resource":{"primary":{"URL":"https:\/\/cardio.jmir.org\/2026\/1\/e103401"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,28]]},"references-count":39,"URL":"https:\/\/doi.org\/10.2196\/103401","relation":{},"ISSN":["2561-1011"],"issn-type":[{"value":"2561-1011","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,28]]},"article-number":"v10i3e103401"}}