ArticleJournal of thoracic disease2026
A bibliometric analysis of artificial intelligence applications in heart failure.
Article in Journal of thoracic disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Heart failure (HF) remains a leading cause of mortality and morbidity worldwide, posing substantial challenges for early and accurate diagnosis as well as personalized therapeutic management. With the rapid evolution of artificial intelligence (AI), substantial progress has been made in the clinical translation and interdisciplinary research of HF. AI offers unprecedented potential to improve the diagnostic accuracy and therapeutic efficacy of HF. However, there is a lack of systematic reviews and analyses in the current research landscape, hotspots, and development trends in this field. This study aimed to employ bibliometric methods to systematically clarify the research status, core research directions, and future prospects of AI applications in HF. Methods: Using the Web of Science Core Collection as the data source, we systematically retrieved literature on AI applications in HF published from 2005 to 2025 and conducted a comprehensive bibliometric analysis via VOSviewer, CiteSpace, and SCImago Graphica. Results: A total of 4,133 records were retrieved initially, among which 4,110 eligible publications were finally included after strict screening. The annual publication output in this field showed accelerated growth since 2019, reaching 1,067 articles in 2025. The United States (1,508 publications) and China (952 publications) ranked as the top two contributing countries. Frontiers in Cardiovascular Medicine was the most prolific journal, whereas Circulation recorded the highest citation frequency. High-frequency keywords included "heart failure, machine learning, AI, risk, and mortality". The mainstream research hotspots concentrated on machine learning algorithms, medical image analysis, and clinical feature extraction. Conclusions: Research on AI applications in HF has undergone rapid development and established a comprehensive research framework covering disease diagnosis, prognosis assessment, and mechanism investigations. Future research priorities should incorporate more multicenter data for external validation, as well as promoting the construction and sharing of multicenter, multimodal datasets. These steps will further enhance the clinical applicability and credibility of AI-driven models in HF management.
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What Socratic 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 Socratic graph.