ReviewFrontiers in cardiovascular medicine2021
Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging.
Review in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 4 of them syntheses that pooled it.
What it found
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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
40 citing papers in PubMed, 4 syntheses or guidelines pooled it, 85 citations in OpenAlex.
- Machine learning for myocarditis diagnosis using cardiovascular magnetic resonance: a systematic review, diagnostic test accuracy meta-analysis, and comparison with human physicians.The international journal of cardiovascular imaging · 2025Pooled it
- Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review.PloS one · 2024Pooled it
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- Prediction models for early neurological deterioration in patients with acute ischemic stroke: a systematic review and critical appraisal.Frontiers in neurologyPooled it
- Beyond the algorithm: why oncology nursing in Asia-Pacific needs evidence-based AI evaluation.Asia-Pacific journal of oncology nursing · 2026Article
- Machine Learning Applications for Risk Stratification in Heart Failure with Preserved Ejection Fraction: A New Era in Cardiology.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Development and Validation of an Explainable Machine Learning Model for Predicting Repeat Catheter Ablation for Atrial Fibrillation: A Single-Center Retrospective Cohort Study.International journal of general medicine · 2026Article
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- Review
- Interpretable multiclassification machine learning models in predicting the extent of small cell lung cancer metastasis.Scientific reports · 2025Article
- Automatic diagnosis of coronary artery stenosis by deep learning based on X-ray coronary angiography.Quantitative imaging in medicine and surgery · 2025Article
- Lectin Microarray-based Glycomics and Machine Learning Identify Shared Osteoarthritis Biomarkers in Humans, Dogs, and Horses.bioRxiv : the preprint server for biology · 2025Article
- Internal Validation of a Machine Learning-Based CDSS for Antimicrobial Stewardship.Life (Basel, Switzerland) · 2025Article
- The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations-a narrative review.Discover oncology · 2025Review
- Artificial Intelligence in Imaging for Personalized Management of Coronary Artery Disease.Journal of clinical medicine · 2025Review
- Enhancing Delirium Prediction and Prevention in Elderly Patients Through Machine Learning-Based Analysis.Sultan Qaboos University medical journal · 2025Article
- Artificial intelligence in heart failure - a comprehensive literature review.Cardiology journal · 2025Review
- Forecasting readmission in COVID-19 patients utilizing blood biomarkers and machine learning in the Hospital-at-Home program.Frontiers in medicine · 2025Article
- Validation of the inadequate delivery of oxygen index in an adult cardiovascular intensive care unit.JTCVS open · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 8 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making in cardiology. The success of these tools is dependent on the understanding of the intrinsic processes being used during the conventional pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous steps to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with the two later tasks of interpretation and decision support, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes in cardiovascular imaging.
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What OpenQuestion holds
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.