On the public citation graph
Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future
0 signed · 274 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1001/jama.2016.17216openalex-snapshot
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
inferredcites →https://doi.org/10.1002/bimj.201100251openalex-snapshot
Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble‐based methods?
inferredcites →https://doi.org/10.1002/ehf2.12529openalex-snapshot
Triage-HF Plus: A Novel Device-Based Remote Monitoring Pathway to Identify Worsening Heart Failure
inferredcites →https://doi.org/10.1002/ehf2.12813openalex-snapshot
Putting AI at the Centre of Heart Failure Care
inferredcites →https://doi.org/10.1002/ejhf.1333openalex-snapshot
Machine Learning-Based Phenogrouping in Heart Failure to Identify Responders to Cardiac Resynchronization Therapy
inferredcites →https://doi.org/10.1002/jmri.25332openalex-snapshot
Quantification of common carotid artery and descending aorta vessel wall thickness from MR vessel wall imaging using a fully automated processing pipeline
inferredcites →https://doi.org/10.1002/jmri.25932openalex-snapshot
Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression
inferredcites →https://doi.org/10.1002/mrm.26631openalex-snapshot
Automatic segmentation of the right ventricle from cardiac MRI using a learning‐based approach
inferredcites →https://doi.org/10.1007/978-0-387-21606-5openalex-snapshot
The Elements of Statistical Learning
inferredcites →https://doi.org/10.1007/978-3-030-75055-8_6openalex-snapshot
Artificial Intelligence and Machine Learning
inferredcites →https://doi.org/10.1007/978-3-319-46723-8_35openalex-snapshot
A Cross-Modality Neural Network Transform for Semi-automatic Medical Image Annotation
inferredcites →https://doi.org/10.1007/978-3-319-46723-8_56openalex-snapshot
Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images
inferredcites →https://doi.org/10.1007/978-3-319-46726-9_31openalex-snapshot
Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network
inferredcites →https://doi.org/10.1007/978-94-009-0279-4_9openalex-snapshot
Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming
inferredcites →https://doi.org/10.1007/978-981-10-9035-6_117openalex-snapshot
A Novel Concept of the Management of Coronary Artery Disease Patients Based on Machine Learning Risk Stratification and Computational Biomechanics: Preliminary Results of SMARTool Project
inferredcites →https://doi.org/10.1007/b107408openalex-snapshot
Data Mining and Knowledge Discovery Handbook
inferredcites →https://doi.org/10.1007/s11548-018-1884-6openalex-snapshot
Automated anatomical labeling of coronary arteries via bidirectional tree LSTMs
inferredcites →https://doi.org/10.1007/s11897-017-0315-2openalex-snapshot
Remote Monitoring to Reduce Heart Failure Readmissions
inferredcites →https://doi.org/10.1016/0735-1097(95)00385-1openalex-snapshot
One-year mortality prognosis in heart failure: A neural network approach based on echocardiographic data
inferredcites →https://doi.org/10.1016/j.acra.2008.04.014openalex-snapshot
Accurate Automatic Papillary Muscle Identification for Quantitative Left Ventricle Mass Measurements in Cardiac Magnetic Resonance Imaging
inferredcites →https://doi.org/10.1016/j.artmed.2010.09.005openalex-snapshot
Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features
inferredcites →https://doi.org/10.1016/j.artmed.2016.08.005openalex-snapshot
A mixed-ensemble model for hospital readmission
inferredcites →https://doi.org/10.1016/j.cardfail.2010.08.002openalex-snapshot
CHAMPION∗ Trial Rationale and Design: The Long-Term Safety and Clinical Efficacy of a Wireless Pulmonary Artery Pressure Monitoring System
inferredcites →https://doi.org/10.1016/j.cmpb.2015.09.001openalex-snapshot
Computer-assisted diagnosis for chronic heart failure by the analysis of their cardiac reserve and heart sound characteristics
inferredcites →https://doi.org/10.1016/j.cmpb.2016.03.020openalex-snapshot
Congestive heart failure detection using random forest classifier
inferredcites →https://doi.org/10.1016/j.echo.2014.05.008openalex-snapshot
Knowledge-Based Reconstruction of Right Ventricular Volumes Using Real-time Three-dimensional Echocardiographic as Well as Cardiac Magnetic Resonance Images: Comparison With a Cardiac Magnetic Resonance Standard
inferredcites →https://doi.org/10.1016/j.echo.2018.12.002openalex-snapshot
Contribution of Cardiovascular Reserve to Prognostic Categories of Heart Failure With Preserved Ejection Fraction: A Classification Based on Machine Learning
inferredcites →https://doi.org/10.1016/j.hlc.2020.09.775openalex-snapshot
768 Machine Learning Prediction Tools for All-Cause Readmissions in Patients Hospitalised for Heart Failure Using Routinely Collected Medical Record Data
inferredcites →https://doi.org/10.1016/j.ijcard.2016.04.149openalex-snapshot
Telemonitoring predicts in advance heart failure admissions
inferredcites →https://doi.org/10.1016/j.ijcard.2017.02.120openalex-snapshot
Evaluation of remote dielectric sensing (ReDS) technology-guided therapy for decreasing heart failure re-hospitalizations
inferredcites →https://doi.org/10.1016/j.jacc.2016.08.062openalex-snapshot
Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography
inferredcites →https://doi.org/10.1016/j.jacc.2018.03.003openalex-snapshot
Smartwatch Algorithm for Automated Detection of Atrial Fibrillation
inferredcites →https://doi.org/10.1016/j.jacc.2019.12.030openalex-snapshot
Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram
inferredcites →https://doi.org/10.1016/j.jbi.2012.04.013openalex-snapshot
Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches
inferredcites →https://doi.org/10.1016/j.jbi.2016.01.009openalex-snapshot
Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function
inferredcites →https://doi.org/10.1016/j.jchf.2016.12.011openalex-snapshot
A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices
inferredcites →https://doi.org/10.1016/j.jclinepi.2012.11.008openalex-snapshot
Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes
inferredcites →https://doi.org/10.1016/j.jelectrocard.2015.04.002openalex-snapshot
Automatic SVM classification of sudden cardiac death and pump failure death from autonomic and repolarization ECG markers
inferredcites →https://doi.org/10.1016/j.jelectrocard.2017.06.008openalex-snapshot
Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone
inferredcites →https://doi.org/10.1016/j.matcom.2020.04.031openalex-snapshot
Application of the residue number system to reduce hardware costs of the convolutional neural network implementation
inferredcites →https://doi.org/10.1016/j.mayocp.2020.01.038openalex-snapshot
Artificial Intelligence in Cardiology: Present and Future
inferredcites →https://doi.org/10.1016/j.media.2016.04.004openalex-snapshot
Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks
inferredcites →https://doi.org/10.1016/j.media.2016.10.007openalex-snapshot
Automatic apical view classification of echocardiograms using a discriminative learning dictionary
inferredcites →https://doi.org/10.1016/j.media.2017.11.008openalex-snapshot
Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis
inferredcites →https://doi.org/10.1016/j.procs.2017.08.265openalex-snapshot
The IoT-based heart disease monitoring system for pervasive healthcare service
inferredcites →https://doi.org/10.1016/j.resuscitation.2008.05.004openalex-snapshot
A review, and performance evaluation, of single-parameter “track and trigger” systems
inferredcites →https://doi.org/10.1016/s0735-1097(18)31187-2openalex-snapshot
CONTINUOUS WEARABLE MONITORING ANALYTICS PREDICT HEART FAILURE DECOMPENSATION: THE LINK-HF MULTI-CENTER STUDY
inferredcites →https://doi.org/10.1037/h0042519openalex-snapshot
The perceptron: A probabilistic model for information storage and organization in the brain.
inferredcites →https://doi.org/10.1038/s41586-020-2145-8openalex-snapshot
Video-based AI for beat-to-beat assessment of cardiac function
inferredcites →https://doi.org/10.1038/s41591-018-0240-2openalex-snapshot
Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram
inferredcites →https://doi.org/10.1038/s41591-018-0268-3openalex-snapshot
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
inferredcites →https://doi.org/10.1038/s41591-021-01335-4openalex-snapshot
Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial
inferredcites →https://doi.org/10.1038/s41746-020-0261-3openalex-snapshot
Artificial intelligence for the diagnosis of heart failure
inferredcites →https://doi.org/10.1093/ehjci/ehaa946.3507openalex-snapshot
mHealth interventions in the management of heart failure, ischaemic heart disease and hypertension: a systematic review and meta-analysis of randomised controlled trials
inferredcites →https://doi.org/10.1093/ehjci/jew328openalex-snapshot
Three-dimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: multicentre validation study
inferredcites →https://doi.org/10.1093/ehjdh/ztaa015openalex-snapshot
Artificial intelligence assessment for early detection of heart failure with preserved ejection fraction based on electrocardiographic features
inferredcites →https://doi.org/10.1093/eurheartj/ehab670openalex-snapshot
Corrigendum to: 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) With the special contribution of the Heart Failure…
inferredcites →https://doi.org/10.1093/eurheartj/ehw128openalex-snapshot
2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure
inferredcites →https://doi.org/10.1093/eurheartj/ehz745.0898openalex-snapshot
P4505VECTOR-HF: The first human experience with the V-LAP, a wireless left atrial pressure monitoring system for patients with heart failure
inferredcites →https://doi.org/10.1097/01.jcn.0000317428.98844.4dopenalex-snapshot
What Do We Know About Adherence and Self-care?
inferredcites →https://doi.org/10.1097/ccm.0000000000001571openalex-snapshot
Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards
inferredcites →https://doi.org/10.1097/cin.0000000000000223openalex-snapshot
Utilizing Home Healthcare Electronic Health Records for Telehomecare Patients With Heart Failure
inferredcites →https://doi.org/10.1097/jcn.0000000000000305openalex-snapshot
A Systematic Review of mHealth-Based Heart Failure Interventions
inferredcites →https://doi.org/10.1097/mlr.0b013e3181de9e17openalex-snapshot
Prediction Modeling Using EHR Data
inferredcites →https://doi.org/10.1097/sla.0000000000002693openalex-snapshot
Artificial Intelligence in Surgery: Promises and Perils
inferredcites →https://doi.org/10.1098/rsif.2017.0821openalex-snapshot
Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances
inferredcites →https://doi.org/10.1109/aiccsa.2008.4493524openalex-snapshot
Intelligent heart disease prediction system using data mining techniques
inferredcites →https://doi.org/10.1109/bigdata.2013.6691760openalex-snapshot
Big data solutions for predicting risk-of-readmission for congestive heart failure patients
inferredcites →https://doi.org/10.1109/cic.2008.4749186openalex-snapshot
Early detection of decompensation conditions in heart failure patients by knowledge discovery: The HEARTFAID approaches
inferredcites →https://doi.org/10.1109/iccnit.2011.6020937openalex-snapshot
Neural Network application in diagnosis of patient: A case study
inferredcites →https://doi.org/10.1109/icmla.2013.158openalex-snapshot
Random Forests on Ubiquitous Data for Heart Failure 30-Day Readmissions Prediction
inferredcites →https://doi.org/10.1109/taeece.2015.7113608openalex-snapshot
Predicting the likelihood of heart failure with a multi level risk assessment using decision tree
inferredcites →https://doi.org/10.1109/tbme.2017.2762762openalex-snapshot
Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images
inferredcites →https://doi.org/10.1109/tbme.2018.2865669openalex-snapshot
Regional Multi-View Learning for Cardiac Motion Analysis: Application to Identification of Dilated Cardiomyopathy Patients
inferredcites →https://doi.org/10.1109/tmi.2016.2538802openalex-snapshot
Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing
inferredcites →https://doi.org/10.1109/tmi.2017.2760978openalex-snapshot
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
inferredcites →https://doi.org/10.1109/tmi.2018.2804799openalex-snapshot
Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT
inferredcites →https://doi.org/10.1109/tmi.2018.2837502openalex-snapshot
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
inferredcites →https://doi.org/10.1109/tmi.2018.2888807openalex-snapshot
Cardiac Phase Detection in Echocardiograms With Densely Gated Recurrent Neural Networks and Global Extrema Loss
inferredcites →https://doi.org/10.1109/tpami.2013.96openalex-snapshot
Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data
inferredcites →https://doi.org/10.1111/j.1751-7133.2011.00220.xopenalex-snapshot
Intrathoracic Impedance vs Daily Weight Monitoring for Predicting Worsening Heart Failure Events: Results of the Fluid Accumulation Status Trial (FAST)
inferredcites →https://doi.org/10.1117/12.2293681openalex-snapshot
Automated Agatston score computation in non-ECG gated CT scans using deep learning
inferredcites →https://doi.org/10.1126/scitranslmed.aaa3487openalex-snapshot
The emerging field of mobile health
inferredcites →https://doi.org/10.1136/heartjnl-2019-315481openalex-snapshot
Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning
inferredcites →https://doi.org/10.1145/1099554.1099670openalex-snapshot
Mining officially unrecognized side effects of drugs by combining web search and machine learning
inferredcites →https://doi.org/10.1145/2783258.2788585openalex-snapshot
Dynamic Hierarchical Classification for Patient Risk-of-Readmission
inferredcites →https://doi.org/10.1148/radiol.2016161315openalex-snapshot
Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study
inferredcites →https://doi.org/10.1152/japplphysiol.00752.2015openalex-snapshot
A machine-learning approach for computation of fractional flow reserve from coronary computed tomography
inferredcites →https://doi.org/10.1155/2012/219080openalex-snapshot
Linear and Nonlinear Heart Rate Variability Indexes in Clinical Practice
inferredcites →https://doi.org/10.1155/2018/5682365openalex-snapshot
A Combined Fully Convolutional Networks and Deformable Model for Automatic Left Ventricle Segmentation Based on 3D Echocardiography
inferredcites →https://doi.org/10.1155/2020/4281243openalex-snapshot
Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques
inferredcites →https://doi.org/10.1155/2020/4972346openalex-snapshot
Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance
inferredcites →https://doi.org/10.1161/circheartfailure.110.958496openalex-snapshot
New Predictive Models of Heart Failure Mortality Using Time-Series Measurements and Ensemble Models
inferredcites →https://doi.org/10.1161/circheartfailure.117.003878openalex-snapshot
Accelerometer-Measured Daily Activity in Heart Failure With Preserved Ejection Fraction
inferredcites →https://doi.org/10.1161/circheartfailure.119.006513openalex-snapshot
Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization
inferredcites →https://doi.org/10.1161/circheartfailure.119.006863openalex-snapshot
Lower Rates of Heart Failure and All-Cause Hospitalizations During Pulmonary Artery Pressure-Guided Therapy for Ambulatory Heart Failure
inferredcites →https://doi.org/10.1161/circimaging.115.004330openalex-snapshot
Cognitive Machine-Learning Algorithm for Cardiac Imaging
inferredcites →https://doi.org/10.1161/circimaging.117.007217openalex-snapshot
Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography–Based Fractional Flow Reserve
inferredcites →https://doi.org/10.1161/circulationaha.114.010637openalex-snapshot
Phenomapping for Novel Classification of Heart Failure With Preserved Ejection Fraction
inferredcites →https://doi.org/10.1161/jaha.118.008678openalex-snapshot
An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest
inferredcites →https://doi.org/10.1177/089431849100400106openalex-snapshot
Effects of Promoting Patients' Participation in Self-Care on Postoperative Recovery and Satisfaction with Care
inferredcites →https://doi.org/10.1177/1541931213601496openalex-snapshot
Managing Heart Failure On the Go: Usability Issues with mHealth Apps for Older Adults
inferredcites →https://doi.org/10.1186/1472-6947-15-s3-s5openalex-snapshot
A multi-layer monitoring system for clinical management of Congestive Heart Failure
inferredcites →https://doi.org/10.1186/s12968-018-0509-0openalex-snapshot
Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification
inferredcites →https://doi.org/10.1186/s40537-019-0268-2openalex-snapshot
Internet of Things is a revolutionary approach for future technology enhancement: a review
inferredcites →https://doi.org/10.1371/journal.pmed.0040104openalex-snapshot
Reducing Medical Costs and Improving Quality via Self-Management Tools
inferredcites →https://doi.org/10.1530/erp-17-0028openalex-snapshot
Automated left heart chamber volumetric assessment using three-dimensional echocardiography in Chinese adolescents
inferredcites →https://doi.org/10.1530/erp-18-0056openalex-snapshot
Artificial intelligence and echocardiography
inferredcites →https://doi.org/10.1536/ihj.19-714openalex-snapshot
Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning
inferredcites →https://doi.org/10.15420/cfr.2019.14openalex-snapshot
Artificial Intelligence, Data Sensors and Interconnectivity: Future Opportunities for Heart Failure
inferredcites →https://doi.org/10.2522/ptj.20130534openalex-snapshot
Perspectives on the Evolution of Mobile (mHealth) Technologies and Application to Rehabilitation
inferredcites →https://doi.org/10.3390/s19071502openalex-snapshot
Detection of Congestive Heart Failure Based on LSTM-Based Deep Network via Short-Term RR Intervals
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4676406openalex-extracted
Investigation on the Abilities of Artificial Intelligence Methods to Accommodate Bias in Medical Diagnostic Data
inferred← citeshttps://doi.org/10.1002/9781394242474.ch12openalex-snapshot
The Integration of Molecular Docking and Machine Learning in Drug Discovery for Neurological Disorders
inferred← citeshttps://doi.org/10.1002/clc.24260openalex-snapshot
Utilizing machine learning for predicting heart failure outcomes: A path toward developing a patient‐centered approach
inferred← citeshttps://doi.org/10.1002/clc.70071openalex-snapshot
Predictive Value of Machine Learning for the Risk of In‐Hospital Death in Patients With Heart Failure: A Systematic Review and Meta‐Analysis
inferred← citeshttps://doi.org/10.1002/mco2.70043openalex-snapshot
LungDiag: Empowering artificial intelligence for respiratory diseases diagnosis based on electronic health records, a multicenter study
inferred← citeshttps://doi.org/10.1002/ncp.11194openalex-snapshot
Role of artificial intelligence in critical care nutrition support and research
inferred← citeshttps://doi.org/10.1002/vetr.70741openalex-snapshot
Assessing the performance of large language models when used to determine ASA status of cats and dogs and generate anaesthetic protocols
inferred← citeshttps://doi.org/10.1007/978-3-031-27499-2_70openalex-snapshot
Automatic Calcium Detection in Echocardiography Based on Deep Learning: A Systematic Review
inferred← citeshttps://doi.org/10.1007/978-3-031-34963-8_5openalex-snapshot
Leveraging Artificial Intelligence Technology for Effective Early Diagnosis: Heart Issues
inferred← citeshttps://doi.org/10.1007/978-3-031-61625-9_33openalex-snapshot
Detecting Heart Failure Relations: A Preliminary Study Integrating HRV, LVEF, and GLS in Patients with Ischemic Heart Disease and Dilated Cardiomyopathy
inferred← citeshttps://doi.org/10.1007/978-3-031-95571-6_1openalex-snapshot
Enhancing Remote Monitoring and Diagnosis of Heart Diseases Through IoT and Machine Learning Integration
inferred← citeshttps://doi.org/10.1007/978-3-032-03394-9_32openalex-snapshot
Development of Predictive Models in Heart Failure
inferred← citeshttps://doi.org/10.1007/978-3-032-12470-8_1openalex-snapshot
Introduction to Artificial Intelligence in Heart Disease Diagnostics
inferred← citeshttps://doi.org/10.1007/978-3-032-15739-3_50openalex-snapshot
The Implications and Consequences of Employing Artificial Intelligence in Nursing Practice: An Integrative Review
inferred← citeshttps://doi.org/10.1007/978-3-662-71115-6_1openalex-snapshot
Einführung: Digital unterstützte Communities als Grundlage der Prävention und Gesundheitsförderung
inferred← citeshttps://doi.org/10.1007/s00210-025-03980-9openalex-snapshot
A comprehensive review on diabetic cardiomyopathy (DCM): histological spectrum, diagnosis, pathogenesis, and management with conventional treatments and natural compounds
inferred← citeshttps://doi.org/10.1007/s00330-023-09785-9openalex-snapshot
Deep learning-based prognostic model using non-enhanced cardiac cine MRI for outcome prediction in patients with heart failure
inferred← citeshttps://doi.org/10.1007/s00521-025-11081-0openalex-snapshot
The paradigm of digital health: AI applications and transformative trends
inferred← citeshttps://doi.org/10.1007/s10741-024-10448-0openalex-snapshot
Machine learning in the prevention of heart failure
inferred← citeshttps://doi.org/10.1007/s11596-024-2960-5openalex-snapshot
Artificial Intelligence in Medical Metaverse: Applications, Challenges, and Future Prospects
inferred← citeshttps://doi.org/10.1007/s11604-024-01702-4openalex-snapshot
Artificial intelligence in fracture detection on radiographs: a literature review
inferred← citeshttps://doi.org/10.1007/s11739-023-03297-6openalex-snapshot
Cardiovascular disease (CVD) outcomes and associated risk factors in a medicare population without prior CVD history: an analysis using statistical and machine learning algorithms
inferred← citeshttps://doi.org/10.1007/s11739-024-03627-2openalex-snapshot
A machine learning-based lung ultrasound algorithm for the diagnosis of acute heart failure
inferred← citeshttps://doi.org/10.1007/s13198-025-03033-7openalex-snapshot
Enhancing arrhythmia detection with ECG signals using a novel stacked vector convolutional network (SVCN)
inferred← citeshttps://doi.org/10.1007/s42399-025-02218-2openalex-snapshot
Insights and Innovations of Artificial Intelligence in Healthcare and Public Health: A Bibliometric Analysis
inferred← citeshttps://doi.org/10.1016/b978-0-443-24790-3.00002-8openalex-snapshot
Advanced sensors for smart healthcare: an introduction
inferred← citeshttps://doi.org/10.1016/b978-0-443-36554-6.00008-8openalex-snapshot
Artificial intelligence-driven personalized medicine
inferred← citeshttps://doi.org/10.1016/j.ajoint.2025.100159openalex-snapshot
Pilot study of ASSORT (AI-based Symptom Stratification in Ophthalmology for Rapid Triage): a triage tool for ophthalmic emergencies
inferred← citeshttps://doi.org/10.1016/j.array.2026.100874openalex-snapshot
Deep learning for heart failure prediction from chest X-ray in AF
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108557openalex-snapshot
Recent advancements and applications of deep learning in heart failure: Α systematic review
inferred← citeshttps://doi.org/10.1016/j.cpcardiol.2024.102797openalex-snapshot
Evaluating ChatGPT platform in delivering heart failure educational material: A comparison with the leading national cardiology institutes
inferred← citeshttps://doi.org/10.1016/j.cvdhj.2023.04.003openalex-snapshot
Artificial intelligence–enabled tools in cardiovascular medicine: A survey of current use, perceptions, and challenges
inferred← citeshttps://doi.org/10.1016/j.dcmed.2022.12.003openalex-snapshot
Artificial intelligence and its application for cardiovascular diseases in Chinese medicine
inferred← citeshttps://doi.org/10.1016/j.ejim.2022.11.029openalex-snapshot
Reshaping care in the aftermath of the pandemic. Implications for cardiology health systems
inferred← citeshttps://doi.org/10.1016/j.glmedi.2024.100109openalex-snapshot
Advancements and applications of Artificial Intelligence in cardiology: Current trends and future prospects
inferred← citeshttps://doi.org/10.1016/j.glmedi.2024.100113openalex-snapshot
Strengths, weaknesses, opportunities and threats (SWOT) analysis of artificial intelligence adoption in nursing care
inferred← citeshttps://doi.org/10.1016/j.hfc.2023.03.005openalex-snapshot
Emerging Roles for Artificial Intelligence in Heart Failure Imaging
inferred← citeshttps://doi.org/10.1016/j.jacadv.2026.102588openalex-snapshot
Design and Architecture of a Generative-AI-Supported, Nonphysician-Delivered Model for GDMT Optimization in HFrEF
inferred← citeshttps://doi.org/10.1016/j.jacc.2022.04.006openalex-snapshot
Is Smoking Cessation the Best Intervention Ever to Prevent Heart Failure?
inferred← citeshttps://doi.org/10.1016/j.measurement.2025.119472openalex-snapshot
Non-invasive cardiovascular and vital signs monitoring techniques: review, challenges, and perspectives
inferred← citeshttps://doi.org/10.1016/j.slast.2025.100361openalex-snapshot
Development and validation of a multimodal machine learning model integrating ultrasound imaging and serum biomarkers for vulnerable carotid plaque prediction
inferred← citeshttps://doi.org/10.1038/s41598-025-99039-zopenalex-snapshot
A novel scoring system for heart failure screening utilizing combined electrocardiogram, phonocardiogram, and radial artery features
inferred← citeshttps://doi.org/10.1063/5.0240419openalex-snapshot
Artificial intelligence (AI) supported patient self-care and consultation in chronic heart failure: A paradigm shift from reactive to predictive, preventive and personalized care
inferred← citeshttps://doi.org/10.1080/01652176.2025.2611847openalex-snapshot
Development of an artificial intelligence-based algorithm for the detection of left atrial enlargement from feline thoracic radiographs
inferred← citeshttps://doi.org/10.1080/14779072.2025.2520830openalex-snapshot
Artificial intelligence: a promising tool for the clinical cardiologist
inferred← citeshttps://doi.org/10.1080/1744666x.2024.2359019openalex-snapshot
Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective
inferred← citeshttps://doi.org/10.1093/ehjdh/ztae064openalex-snapshot
Digital solutions to optimize guideline-directed medical therapy prescription rates in patients with heart failure: a clinical consensus statement from the ESC Working Group on e-Cardiology, the Heart Failure Association of the European Society of Cardiology, the Association of Cardiovascular…
inferred← citeshttps://doi.org/10.1093/ehjdh/ztaf132openalex-snapshot
Deep learning algorithm for detection of acute heart failure using standard ECG waveforms
inferred← citeshttps://doi.org/10.1097/jcn.0000000000001162openalex-snapshot
A Life Course Approach to Cardiovascular Disease Prevention
inferred← citeshttps://doi.org/10.1097/js9.0000000000002481openalex-snapshot
Transforming healthcare: the impact of artificial intelligence on diagnostics, pharmaceuticals, and ethical considerations – a comprehensive review
inferred← citeshttps://doi.org/10.1097/md.0000000000041766openalex-snapshot
Big data analytics and machine learning in hematology: Transformative insights, applications and challenges
inferred← citeshttps://doi.org/10.1097/ms9.0000000000002138openalex-snapshot
Machine learning in heart failure diagnosis, prediction, and prognosis: review
inferred← citeshttps://doi.org/10.1097/ms9.0000000000003349openalex-snapshot
A narrative review on ethical considerations and challenges in AI-driven cardiology
inferred← citeshttps://doi.org/10.1097/ms9.0000000000004812openalex-snapshot
The role of artificial intelligence in early detection and risk prediction of ischemic heart disease
inferred← citeshttps://doi.org/10.1097/ms9.0000000000005235openalex-snapshot
Mapping the research landscape of artificial intelligence in heart failure: a bibliometric analysis
inferred← citeshttps://doi.org/10.1097/scs.0000000000010210openalex-snapshot
A Systematic Review of the Application of Computational Technology in Microtia
inferred← citeshttps://doi.org/10.1109/access.2025.3573760openalex-snapshot
Cardiac Clarity: Harnessing Machine Learning for Accurate Heart-Disease Prediction
inferred← citeshttps://doi.org/10.1109/access.2026.3670331openalex-snapshot
Deep Learning-Based Echocardiography Wall Thickness Analysis for Heart Failure Detection
inferred← citeshttps://doi.org/10.1109/bibe63649.2024.10820487openalex-snapshot
AI-Driven Decision Support System for Heart Failure Diagnosis: INTELHEART Approach Towards Personalized Treatment Strategies
inferred← citeshttps://doi.org/10.1109/icaiihi57871.2023.10489712openalex-snapshot
Patient Engagement and Satisfaction in Ai-Enhanced Healthcare Management
inferred← citeshttps://doi.org/10.1109/icercs63125.2024.10895279openalex-snapshot
AI-Integrated Probabilistic Neuro-Fuzzy TemporalFusionNet for Robotic IoMT Automation in Chronic Kidney Disease Detection and Prediction
inferred← citeshttps://doi.org/10.1109/icetecc65365.2025.11070268openalex-snapshot
Enhancing Heart Disease Classification Accuracy and Computational Efficiency Using Machine Learning and Feature Optimization
inferred← citeshttps://doi.org/10.1109/icidca66325.2025.11280335openalex-snapshot
Continuous and Accurate Heart Attack Risk Assessment via a Real-Time Data Pipeline with Data Fusion and Spark-based Machine Learning
inferred← citeshttps://doi.org/10.1109/icimcis56303.2022.10017552openalex-snapshot
Problem Identification on Cardiovascular Disease Prevention using Artificial Intelligence: A Literature Review
inferred← citeshttps://doi.org/10.1109/icmi65310.2025.11141338openalex-snapshot
Artificial Intelligence to Analyze Auscultation Signals to Detect PDA Heart Defect
inferred← citeshttps://doi.org/10.1109/ictacs62700.2024.10841304openalex-snapshot
Advancements in Heart Disease Detection: Integrating Machine Learning Algorithms and Clinical Insights
inferred← citeshttps://doi.org/10.1109/ictbig59752.2023.10456055openalex-snapshot
Bibliometric Analysis of Application of Artificial Intelligence in Heart Disease: 2013 to 2023
inferred← citeshttps://doi.org/10.1109/iiai-aai-winter61682.2023.00042openalex-snapshot
Detection of Cardiovascular Disease Using Machine Learning Algorithms and Principal Component Analysis
inferred← citeshttps://doi.org/10.1111/hdi.13033openalex-snapshot
Artificial intelligence and digital health for volume maintenance in hemodialysis patients
inferred← citeshttps://doi.org/10.1111/jan.70456openalex-snapshot
Artificial Intelligence‐Based Delirium Prediction Model for Post‐Cardiac Surgery Patients: A Scoping Review
inferred← citeshttps://doi.org/10.11591/ijeecs.v32.i2.pp1115-1122openalex-snapshot
An early warning system of heart failure mortality with combined machine learning methods
inferred← citeshttps://doi.org/10.1161/jaha.124.036946openalex-snapshot
Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review
inferred← citeshttps://doi.org/10.1161/jaha.124.039511openalex-snapshot
Artificial Intelligence in Diagnosis of Heart Failure
inferred← citeshttps://doi.org/10.1177/17539447251406847openalex-snapshot
Artificial intelligence for cardiology: from diagnosis to management
inferred← citeshttps://doi.org/10.1177/20552076261487568openalex-snapshot
Dynamic alignment of large language models for evidence-grounded heart failure decision support
inferred← citeshttps://doi.org/10.1186/s12872-025-04642-7openalex-snapshot
Development and validation of an integrated prognostic model for all-cause mortality in heart failure: a comprehensive analysis combining clinical, electrocardiographic, and echocardiographic parameters
inferred← citeshttps://doi.org/10.1186/s12893-025-02802-2openalex-snapshot
Axillary lymph node metastasis in breast cancer: from historical axillary surgery to updated advances in the preoperative diagnosis and axillary management
inferred← citeshttps://doi.org/10.1186/s12962-025-00695-9openalex-snapshot
A digital diagnostic pathway for heart failure: an economic evaluation
inferred← citeshttps://doi.org/10.1186/s13012-024-01346-yopenalex-snapshot
Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions
inferred← citeshttps://doi.org/10.1186/s13019-026-04003-1openalex-snapshot
Left ventricular assist devices and right heart dysfunction: a review of impact mechanisms, evaluation methods, and prevention and management
inferred← citeshttps://doi.org/10.1253/jjcsc.31.0_40openalex-snapshot
慢性心不全の新分類に向けたフェノマッピング─CHART-2 AI研究より─
inferred← citeshttps://doi.org/10.12677/acm.2024.1451623openalex-snapshot
Research Progress on the Application of Chest Pain Risk Score in Risk Stratification of Patients with Acute Chest Pain
inferred← citeshttps://doi.org/10.1371/journal.pone.0285512openalex-snapshot
Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus
inferred← citeshttps://doi.org/10.14423/smj.0000000000001896openalex-snapshot
Bridging the Gap: Practical Application of Artificial Intelligence Technologies in Routine Clinical Practice
inferred← citeshttps://doi.org/10.14740/cr1434openalex-snapshot
Assessment of Volume Status in Hospitalized Patients With Chronic Heart Failure
inferred← citeshttps://doi.org/10.1515/med-2023-0752openalex-snapshot
SMOC2 plays a role in heart failure via regulating TGF-β1/Smad3 pathway-mediated autophagy
inferred← citeshttps://doi.org/10.15644/asc57/1/8openalex-snapshot
Artificial Intelligence in Medicine and Dentistry
inferred← citeshttps://doi.org/10.17116/profmed20252807136openalex-snapshot
Innovative approach for cardiovascular risk predicting by machine learning methods using
inferred← citeshttps://doi.org/10.21037/cdt-22-438openalex-snapshot
Cardiovascular disease/stroke risk stratification in deep learning framework: a review
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3110088/v1openalex-snapshot
Artificial Intelligence Applications in Decision Making for Disease Management
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9498049/v1openalex-snapshot
Heart Failure Prediction & Risk Stratification using Machine Learning
inferred← citeshttps://doi.org/10.2139/ssrn.6757316openalex-snapshot
Electrocardiogram-Based Deep Learning for Time-Resolved Prediction of Heart Failure With Reduced Ejection Fraction: A Multinational Study
inferred← citeshttps://doi.org/10.2174/011573403x334095241205041550openalex-snapshot
Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems
inferred← citeshttps://doi.org/10.2174/011573403x369978250818060357openalex-snapshot
The Narrative Review: Advancements in Heart Failure Diagnosis and Management using Artificial Intelligence: A New Era of Patient Care
inferred← citeshttps://doi.org/10.2174/0126662558348090241210063629openalex-snapshot
Artificial Intelligence for Cardiovascular Diseases
inferred← citeshttps://doi.org/10.2196/54556openalex-snapshot
Leadership for AI Transformation in Health Care Organization: Scoping Review
inferred← citeshttps://doi.org/10.2196/preprints.50634openalex-snapshot
Application of Artificial Intelligence in Three-level Prevention of Heart Failure (Preprint)
inferred← citeshttps://doi.org/10.2196/preprints.63885openalex-snapshot
Digital Transformation in Healthcare - A Recent Progress and Applications of Artificial Intelligence in Healthcare (Preprint)
inferred← citeshttps://doi.org/10.2478/jtim-2023-0117openalex-snapshot
Analysis and prediction of research hotspots and trends in heart failure research.
inferred← citeshttps://doi.org/10.25259/gjms.2022.v2i3.91openalex-snapshot
Insights into the Potential Role of Artificial Intelligence in Diagnosis of Heart Failure
inferred← citeshttps://doi.org/10.26634/jip.11.1.20617openalex-snapshot
Impact of artificial intelligence in medical imaging
inferred← citeshttps://doi.org/10.30702/ujcvs/23.31(04)/km056-5762openalex-snapshot
Laboratory Predictors of Mortality in Patients with Heart Failure and Reduced Left Ventricular Ejection Fraction
inferred← citeshttps://doi.org/10.31435/ijitss.1(49).2026.4733openalex-snapshot
THE ROLE OF ARTIFICIAL INTELLIGENCE IN EARLY DETECTION OF HEART DISEASES: SOCIAL IMPLICATIONS AND ETHICAL CHALLENGES
inferred← citeshttps://doi.org/10.31782/ijcrr.2023.151702openalex-snapshot
Artificial Intelligence in Medical Diagnostics and Drug discovery: A Review
inferred← citeshttps://doi.org/10.3233/faia240165openalex-snapshot
Machine Learning Approach to the Classification of Tensiometric Blood Test Results for Chronic Heart Failure Diagnosis
inferred← citeshttps://doi.org/10.3389/fcvm.2026.1714589openalex-snapshot
Deep learning-based early prediction of life-threatening ventricular arrhythmias using long-term Holter ECG signals
inferred← citeshttps://doi.org/10.3389/fcvm.2026.1721700openalex-snapshot
Ultrasound in cardiovascular care: a perspective on preventive, diagnostic, and monitoring applications
inferred← citeshttps://doi.org/10.3389/fdgth.2025.1552372openalex-snapshot
Artificial intelligence in nursing: an integrative review of clinical and operational impacts
inferred← citeshttps://doi.org/10.3389/fdgth.2025.1690223openalex-snapshot
Artificial intelligence in transitional care: practice, promise, and pitfalls—a scoping review
inferred← citeshttps://doi.org/10.3389/fendo.2024.1385836openalex-snapshot
Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography
inferred← citeshttps://doi.org/10.3389/fmed.2024.1505692openalex-snapshot
Evolution of artificial intelligence in healthcare: a 30-year bibliometric study
inferred← citeshttps://doi.org/10.3389/fmed.2025.1553970openalex-snapshot
A bibliometric analysis of artificial intelligence research in critical illness: a quantitative approach and visualization study
inferred← citeshttps://doi.org/10.3389/fmed.2025.1602077openalex-snapshot
Machine learning based insights into cardiomyopathy and heart failure research: a bibliometric analysis from 2005 to 2024
inferred← citeshttps://doi.org/10.3389/frai.2025.1638904openalex-snapshot
The potential of DeepSeek for AI-aided diagnosis of antibody-positive autoimmune encephalitis: a single-center, retrospective, observational study
inferred← citeshttps://doi.org/10.3390/app13137663openalex-snapshot
Will Artificial Intelligence Provide Answers to Current Gaps and Needs in Chronic Heart Failure?
inferred← citeshttps://doi.org/10.3390/biology12081123openalex-snapshot
Functional Cardiovascular Characterization of the Common Marmoset (Callithrix jacchus)
inferred← citeshttps://doi.org/10.3390/clinpract15010017openalex-snapshot
Artificial Intelligence in Cardiac Surgery: Transforming Outcomes and Shaping the Future
inferred← citeshttps://doi.org/10.3390/diagnostics15121561openalex-snapshot
Quantitative Analysis of Diagnostic Reasoning Using Initial Electronic Medical Records
inferred← citeshttps://doi.org/10.3390/diagnostics16101572openalex-snapshot
Enhanced Prediction of Cardiovascular Disease Through Integrated Machine Learning Models Combining Clinical and Demographic Characteristics
inferred← citeshttps://doi.org/10.3390/electronics13050866openalex-snapshot
Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review
inferred← citeshttps://doi.org/10.3390/healthcare12020125openalex-snapshot
Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives
inferred← citeshttps://doi.org/10.3390/ijerph191811413openalex-snapshot
A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence
inferred← citeshttps://doi.org/10.3390/jcdd10120485openalex-snapshot
Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look
inferred← citeshttps://doi.org/10.3390/jcdd11010015openalex-snapshot
Heartfelt Breakthroughs: Elevating Quality of Life with Cutting-Edge Advances in Heart Failure Treatment
inferred← citeshttps://doi.org/10.3390/jcdd13090415openalex-snapshot
Heart Failure in Focus: Acute Presentation, Risk Factors and Integration of Artificial Intelligence
inferred← citeshttps://doi.org/10.3390/jcm13092592openalex-snapshot
Heart Failure Management through Telehealth: Expanding Care and Connecting Hearts
inferred← citeshttps://doi.org/10.3390/jcm14061951openalex-snapshot
Eligibility of Outpatients with Chronic Heart Failure for Vericiguat and Omecamtiv Mecarbil: From Clinical Trials to the Real-World Practice
inferred← citeshttps://doi.org/10.3390/jcm14248824openalex-snapshot
Artificial Intelligence and the Future of Cardiac Implantable Electronic Devices: Diagnostics, Monitoring, and Therapy
inferred← citeshttps://doi.org/10.3390/jimaging10080193openalex-snapshot
Revolutionizing Cardiac Imaging: A Scoping Review of Artificial Intelligence in Echocardiography, CTA, and Cardiac MRI
inferred← citeshttps://doi.org/10.3390/life14010145openalex-snapshot
Artificial Intelligence in Heart Failure: Friend or Foe?
inferred← citeshttps://doi.org/10.3390/sym17030469openalex-snapshot
AI-Driven Technology in Heart Failure Detection and Diagnosis: A Review of the Advancement in Personalized Healthcare
inferred← citeshttps://doi.org/10.33915/etd.11325openalex-snapshot
Amelioration of Mitochondrial Bioenergetic Dysfunction in Diabetes Mellitus: Delving into Specialized and Non-specific Therapeutics for the Ailing Heart
inferred← citeshttps://doi.org/10.34133/jbioxresearch.0022openalex-snapshot
Artificial Intelligence in Revolutionizing Kidney Care and Beyond: Kid-AI Revolution
inferred← citeshttps://doi.org/10.36502/2026/asjbccr.6448openalex-snapshot
Advances in the Diagnosis and Treatment of Cardiovascular Diseases and Their Impact on Perioperative Anesthetic Strategies
inferred← citeshttps://doi.org/10.36628/ijhf.2023.0050openalex-snapshot
Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future
inferred← citeshttps://doi.org/10.36660/abc.20240740openalex-snapshot
Uso da Inteligência Artificial Aplicada ao Eletrocardiograma para Diagnóstico de Disfunção Sistólica Ventricular Esquerda
inferred← citeshttps://doi.org/10.36660/abc.20240740iopenalex-snapshot
Use of Artificial Intelligence Applied to Electrocardiogram for Diagnosis of Left Ventricular Systolic Dysfunction
inferred← citeshttps://doi.org/10.36660/abchf.20230031openalex-snapshot
The Role of Artificial Intelligence and 3D Printing in Advancing the Diagnosis and Treatment of Cardiomyopathies
inferred← citeshttps://doi.org/10.37349/emed.2025.1001347openalex-snapshot
Artificial intelligence in cardiovascular prognosis and diagnosis: a review
inferred← citeshttps://doi.org/10.4018/979-8-3373-4222-1.ch019openalex-snapshot
AI and Mental Health Innovations in Detection, Prevention, and Support for Learners
inferred← citeshttps://doi.org/10.4018/979-8-3693-8990-4.ch006openalex-snapshot
Medical Imaging and Artificial Intelligence
inferred← citeshttps://doi.org/10.4018/979-8-3693-9735-0.ch002openalex-snapshot
Artificial Intelligence in Cardiovascular Healthcare
inferred← citeshttps://doi.org/10.4081/gc.2026.11708openalex-snapshot
Heart failure patients: what does it change in the elderly?
inferred← citeshttps://doi.org/10.4103/jcecho.jcecho_29_26openalex-snapshot
Artificial Intelligence and Echocardiography: From Literacy to Leadership, Redefining Roles, Responsibilities, and Sustainability in the Artificial Intelligence-driven Echocardiography Era
inferred← citeshttps://doi.org/10.4103/jcecho.jcecho_45_26openalex-snapshot
Artificial Intelligence in Cardiovascular Imaging: Integration with Clinical and Nonimaging Data
inferred← citeshttps://doi.org/10.47385/tudoeciencia.966.2023openalex-snapshot
O uso da inteligência artificial como ferramenta de diagnóstico radiológico
inferred← citeshttps://doi.org/10.52403/gijhsr.20230209openalex-snapshot
Machine Learning and the Future of Preventative Cardiology: A Look at Early Detection Techniques
inferred← citeshttps://doi.org/10.53555/jptcp.v30i19.3751openalex-snapshot
ARTIFICIAL INTELLIGENCE IN CARDIOVASCULAR DISEASES: A SYSTEMIC REVIEW AND META-ANALYSIS
inferred← citeshttps://doi.org/10.5603/cj.98650openalex-snapshot
Advancements in artificial intelligence-driven techniques for interventional cardiology
inferred← citeshttps://doi.org/10.5812/semj-161280openalex-snapshot
The Current State of Artificial Intelligence in Healthcare: A Narrative Review of Opportunities and Challenges
inferred← citeshttps://doi.org/10.5937/scriptamed57-62859openalex-snapshot
Transforming heart failure care through AI: Advances and future directions
inferred← citeshttps://doi.org/10.59598/me-2305-6053-2025-116-3-15-27openalex-snapshot
POSSIBILITIES OF ARTIFICIAL INTELLIGENCE IN HEART FAILURE DIAGNOSIS
inferred← citeshttps://doi.org/10.61186/ijbc.15.3.68openalex-snapshot
The application of artificial intelligence (AI) in the diagnosis of platelet disorders
inferred← citeshttps://doi.org/10.62486/latia202313openalex-snapshot
Implementation of a Machine Learning Algorithm for the Detection of Cardiovascular Diseases in Adult Patients in Public Hospitals of Lima, Peru, 2023
inferred← citeshttps://doi.org/10.62906/bs.book.397openalex-snapshot
Cardiovascular Medical Devices: Technology and Innovation
inferred← citeshttps://doi.org/10.70749/ijbr.v3i2.667openalex-snapshot
The Role of Artificial Intelligence in Early Diagnosis and Management of Cardiovascular Diseases
inferred← citeshttps://doi.org/10.7759/cureus.45255openalex-snapshot
Clinicians’ Views on Using Artificial Intelligence in Healthcare: Opportunities, Challenges, and Beyond
inferred← citeshttps://doi.org/10.7759/cureus.59661openalex-snapshot
Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review
inferred← citeshttps://doi.org/10.7759/cureus.66925openalex-snapshot
Application of Artificial Intelligence in Cardiology: A Bibliometric Analysis
inferred← citeshttps://doi.org/10.7759/cureus.87130openalex-snapshot
The Role of Artificial Intelligence in Heart Failure Diagnostics, Risk Prediction, and Therapeutic Strategies: A Comprehensive Review
inferred← citeshttps://doi.org/10.7759/cureus.94920openalex-snapshot
AI Tools for Heart Failure Management: A Comprehensive Review of Potential, Pitfalls, and Predictive Analytics
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.