ReviewJournal of cardiovascular development and disease2023
Artificial Intelligence Technologies in Cardiology.
Review in Journal of cardiovascular development and disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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.
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Who cites it
28 citing papers in PubMed.
- A machine learning approach predicts improvement of physical exercise capacity based on pulse wave analysis in coronary artery disease patients.Journal of sport and health science · 2026Article
- Establishment and validation of a machine learning model to stratify malnutrition risk in hospitalized older patients with chronic heart failure.Asia Pacific journal of clinical nutrition · 2026Article
- An Integrative Review of the Cardiovascular Disease Spectrum: Integrating Multi-Omics and Artificial Intelligence for Precision Cardiology.Diseases (Basel, Switzerland) · 2026Review
- Machine learning in cardiology education: preparing the next generation for the AI era.The British journal of cardiology · 2026Article
- Combined Applications of Artificial Intelligence and Simulation for Healthcare Process Optimization: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Estimating LVEF from ECG with GPT-4o Fine-Tuned Vision: A Novel Approach in AI-Driven Cardiac Diagnostics.Journal of medical systems · 2025Article
- From Clinic to Cloud: Efficacy of AI-Assisted Remote Monitoring of Patients With Implantable Cardiac Devices.Pacing and clinical electrophysiology : PACE · 2025Review
- Artificial Intelligence in Cardiovascular Health: Insights into Post-COVID Public Health Challenges.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2025Review
- Review
- Comparative Efficacy of ChatGPT and DeepSeek in Addressing Patient Queries on Gonarthrosis and Total Knee Arthroplasty.Arthroplasty today · 2025Article
- Predicting Mortality in Atrial Fibrillation Patients Treated with Direct Oral Anticoagulants: A Machine Learning Study Based on the MIMIC-IV Database.Journal of clinical medicine · 2025Article
- Review
- LOLATAO-An Artificial-Intelligence-Based Virtual Assistant for Clinical Follow-Up of Patients with Non-Valvular Atrial Fibrillation (AF) Undergoing Oral Anticoagulant Therapy (OAT): A Feasibility Study.Journal of clinical medicine · 2025Article
- Bibliometric analysis of artificial intelligence applications in cardiovascular imaging: trends, impact, and emerging research areas.Annals of medicine and surgery (2012) · 2025Article
- Artificial intelligence in cardiovascular procedures: a bibliometric and visual analysis study.Annals of medicine and surgery (2012) · 2025Review
- Review
- Clinical Value of Nomogram Model based on Multimodality Ultrasound Image Characteristics Differentiating Benign and Malignant Breast Masses.Current medical imaging · 2025Article
- Artificial Intelligence Advancements in Cardiomyopathies: Implications for Diagnosis and Management of Arrhythmogenic Cardiomyopathy.Current heart failure reports · 2024Review
- Comprehensive Analysis of Cardiovascular Diseases: Symptoms, Diagnosis, and AI Innovations.Bioengineering (Basel, Switzerland) · 2024Review
- The Role of Artificial Intelligence and Machine Learning in Cardiovascular Imaging and Diagnosis.Cureus · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the world produces exabytes of data, there is a growing need to find new methods that are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant potential to impact the healthcare industry, which is already on the road to change with the digital transformation of vast quantities of information. The implementation of AI has already achieved success in the domains of molecular chemistry and drug discoveries. The reduction in costs and in the time needed for experiments to predict the pharmacological activities of new molecules is a milestone in science. These successful applications of AI algorithms provide hope for a revolution in healthcare systems. A significant part of artificial intelligence is machine learning (ML), of which there are three main types-supervised learning, unsupervised learning, and reinforcement learning. In this review, the full scope of the AI workflow is presented, with explanations of the most-often-used ML algorithms and descriptions of performance metrics for both regression and classification. A brief introduction to explainable artificial intelligence (XAI) is provided, with examples of technologies that have developed for XAI. We review important AI implementations in cardiology for supervised, unsupervised, and reinforcement learning and natural language processing, emphasizing the used algorithm. Finally, we discuss the need to establish legal, ethical, and methodical requirements for the deployment of AI models in medicine.
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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.