ReviewCurrent cardiology reviews2026
The Narrative Review: Advancements in Heart Failure Diagnosis and Management using Artificial Intelligence: A New Era of Patient Care.
Review in Current cardiology reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Mapping the research landscape of artificial intelligence in heart failure: a bibliometric analysis.Annals of medicine and surgery (2012) · 2026Article
- Integrated bioinformatics analysis of molecular signatures and therapeutic targets in early sepsis.Frontiers in cellular and infection microbiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart Failure (HF) is a prevalent medical illness worldwide that affects millions and is a substantial economic burden. Its epidemiological impact is on the rise due to factors such as the ageing of the population, increasing rates of diabetes and hypertension, and better survival post-myocardial infarction. Some limitations in HF management include diagnostic challenges, sudden progression of the disease, and rising rates of readmission. Continuous monitoring and limited therapeutic interventions add further complexity to care. Artificial Intelligence(AI) is essential in health care and has provided solutions for improving HF management. Techniques like machine learning and deep learning enhance clinical decision-making and patient care. AI helps physicians diagnose HF more precisely through the analysis of imaging and electrocardiograms. Additionally, the patients' risk is calculated using various AI algorithms to develop personalized treatments for each individual. AI will help healthcare providers identify problems early and select appropriate therapies, leading to better outcomes. Further areas for improvement include enhanced data integration, predictive accuracy, patient engagement, data privacy and ethics, as well as integration with clinical workflows. AI technologies will continue to evolve in managing and treating HF; ongoing exploration and development are crucial for its optimization. This review outlines the current progress and potential of AI in the future to ensure better patient care and healthcare practices.
Indexed as
Identifiers
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.