Observational studyNature medicine2025
Implementation of a national AI technology program on cardiovascular outcomes and the health system.
Observational study in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Artificial Intelligence Platform Architecture for Hospital Systems: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Advanced quantitative CT for coronary artery disease: integrating stenosis quantification, plaque quantification, and fractional flow reserve-CT.The British journal of radiology · 2026Review
- Comparative validation and clinical utility of an artificial intelligence-based CT-SYNTAX score in complex coronary artery disease.The international journal of cardiovascular imaging · 2026Article
- Great debate: the new risk factor-weighted clinical likelihood model is useful to estimate the initial pre-test probability of obstructive coronary artery disease in individuals with suspected chronic coronary syndromes.European heart journal · 2026Article
- Digital heart initiative: an ecosystem for digital discovery and precision medicine in cardiology.National science review · 2026Article
- Impact of artificial intelligence on cardiovascular workflow, engagement, and outcomes: a systematic review.NPJ digital medicine · 2026Article
- New Risk Factor-Weighted Clinical Likelihood (RF-CL) in Diagnosis of Chronic Coronary Syndrome in Men and Women: Our First Impressions About the Differences and Opportunities.Journal of clinical medicine · 2026Article
- Artificial intelligence-enabled coronary plaque quantification for personalized risk assessment and lipid-lowering therapy: Insights from the FISH&CHIPS studyAmerican journal of preventive cardiology · 2026Article
- Implementation and learning curve in AI-assisted fluid management during abdominal oncologic surgery: a retrospective observational study.Journal of anesthesia, analgesia and critical care · 2026Article
- Assessment of Fractional Flow Reserve from Coronary CT Angiography Using a Deep Learning-Based Algorithm: A Multicenter Retrospective Study.Diagnostics (Basel, Switzerland) · 2026Article
- WHF Roadmap for Integrated Care in People Living with - or at Risk of - Cardiovascular Disease and Multiple Long-Term Conditions.Global heart · 2026Review
- Digital Twins in Personalized Medicine: Bridging Innovation and Clinical Reality.Journal of personalized medicine · 2025Review
- Cognitive alignment in cardiovascular AI: designing predictive models that think with, not just for, clinicians.Frontiers in cardiovascular medicine · 2025Article
- Unsupervised Transformer Learning for Rapid and High-Quality MRI Data Acquisition.Health data science · 2025Article
- Efficacy of artificial intelligence-based FFR technology for coronary CTA stenosis detection in clinical management of coronary artery disease: a systematic review.Frontiers in physiology · 2025Review
- Article
- Cardiovascular magnetic resonance versus coronary computed tomography angiography with fractional flow reserve for diagnosing obstructive coronary artery disease in higher risk patients: rationale and design of CONCORD-A prospective, single-center diagnostic accuracy study.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
Corrections and comments
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Authors and funding
39 authors.
Funding
Abstract
Coronary artery disease (CAD) is a major cause of ill health and death worldwide. Coronary computed tomographic angiography (CCTA) is the first-line investigation to detect CAD in symptomatic patients. This diagnostic approach risks greater second-line heart tests and treatments at a cost to the patient and health system. The National Health Service funded use of an artificial intelligence (AI) diagnostic tool, computed tomography (CT)-derived fractional flow reserve (FFR-CT), in patients with chest pain to improve physician decision-making and reduce downstream tests. This observational cohort study assessed the impact of FFR-CT on cardiovascular outcomes by including all patients investigated with CCTA during the national AI implementation program at 27 hospitals (CCTA n = 90,553 and FFR-CT n = 7,863). FFR-CT was safe, with no difference in all-cause (n = 1,134 (3.2%) versus 1,612 (2.9%), adjusted-hazard ratio (aHR) 1.00 (0.93-1.08), P = 0.97) or cardiovascular mortality (n = 465 (1.3%) versus 617 (1.1%), aHR 0.96 (0.85-1.08), P = 0.48), while reducing invasive coronary angiograms (n = 5,720 (16%) versus 8,183 (14.9%), aHR 0.93 (0.90-0.97), P < 0.001) and noninvasive cardiac tests (189/1,000 patients versus 167/1,000), P < 0.001). Implementation of an AI-diagnostic tool as part of a health intervention program was safe and beneficial to the patient pathway and health system with fewer cardiac tests at 2 years.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.