Research graph
References from Operationalizing AI-Enabled Cardiovascular Biomarkers: A Clinician-Centered Framework for Validation, Governance, and Workflow Integration. Local targets link to admitted publications; unresolved targets remain external evidence.
Biomarkers and surrogate endpoints: preferred definitions and conceptual framework
10.1067/mcp.2001.113989 · External reference
Benchmarks for the assessment of novel cardiovascular biomarkers
10.1161/circulationaha.106.683110 · External reference
Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association
10.1161/circulationaha.109.192278 · External reference
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
10.1161/cir.0000000000001401 · External reference
Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management
10.1038/s41569-020-00445-9 · External reference
An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction
10.1016/s0140-6736(19)31721-0 · External reference
Large-scale assessment of a smartwatch to identify atrial fibrillation
10.1056/nejmoa1901183 · External reference
Transforming cardiovascular care with digital health: the past, progress, and promise
10.1016/j.jacadv.2024.101183 · External reference
Digital transformation in the diagnostics and therapy of cardiovascular diseases: comprehensive literature review
10.2196/44983 · External reference
AI-driven real-time monitoring of cardiovascular conditions with wearable devices: scoping review
10.2196/73846 · External reference
Artificial intelligence-based clinical decision support systems in cardiovascular diseases
10.14744/anatoljcardiol.2023.3685 · External reference
Artificial intelligence-based electrocardiographic biomarker for outcome prediction in patients with acute heart failure: prospective cohort study
10.2196/52139 · External reference
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Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)
10.1038/s41746-020-0260-4 · External reference
V3+ extends the V3 framework to ensure user-centricity and scalability of sensor-based digital health technologies
10.1038/s41746-024-01322-2 · External reference
10.17226/12869
10.17226/12869 · External reference
Waste, leaks, and failures in the biomarker pipeline
10.1373/clinchem.2016.254649 · External reference
The translation and commercialisation of biomarkers for cardiovascular disease-a review
10.3389/fcvm.2022.897106 · External reference
Clinical translation of cardiovascular biomarkers - a long and uncertain road
10.1056/evide2200222 · External reference
A simple, step-by-step guide to interpreting decision curve analysis
10.1186/s41512-019-0064-7 · External reference
Multiomics approaches to cardiovascular disease: technological innovations and clinical translation
10.1152/ajpheart.00024.2026 · External reference
Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond
10.1002/sim.2929 · External reference
PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods
10.1136/bmj-2024-082505 · External reference
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · External reference
FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare
10.1136/bmj-2024-081554 · External reference
Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
10.1038/s41591-022-01772-9 · External reference
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Dissecting racial bias in an algorithm used to manage the health of populations
10.1126/science.aax2342 · External reference
Ensuring fairness in machine learning to advance health equity
10.7326/m18-1990 · External reference
Importance of external validation and subgroup analysis of artificial intelligence in the detection of low ejection fraction from electrocardiograms
10.1093/ehjdh/ztac065 · External reference
Pragmatic approaches to the evaluation and monitoring of artificial intelligence in health care: a science advisory from the American Heart Association
10.1161/cir.0000000000001400 · External reference
Do no harm: a roadmap for responsible machine learning for health care
10.1038/s41591-019-0548-6 · External reference
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Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram
10.1038/s41591-018-0240-2 · External reference
Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial
10.1038/s41591-021-01335-4 · External reference
