ReviewHeliyon2024
Artificial intelligence and atrial fibrillation: A bibliometric analysis from 2013 to 2023.
Review in Heliyon, 2024. 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.
- From PD-1/PD-L1 to tertiary lymphoid structures: Paving the way for precision immunotherapy in cholangiocarcinoma treatment.Human vaccines & immunotherapeutics · 2025Article
- AI-Powered Precision: Revolutionizing Atrial Fibrillation Detection with Electrocardiograms.Journal of clinical medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: In the study of atrial fibrillation (AF), a prevalent cardiac arrhythmia, the utilization of artificial intelligence (AI) in diagnostic and therapeutic strategies holds the potential to address existing limitations. This research employs bibliometrics to objectively investigate research hotspots, development trends, and existing issues in the application of AI within the AF field, aiming to provide targeted recommendations for relevant researchers. Methods: Relevant publications on the application of AI in AF field were retrieved from the Web of Science Core Collection (WoSCC) database from 2013 to 2023. The bibliometric analysis was conducted by the R (4.2.2) "bibliometrix" package and VOSviewer(1.6.19). Results: Analysis of 912 publications reveals that the field of AI in AF is currently experiencing rapid development. The United States, China, and the United Kingdom have made outstanding contributions to this field. Acharya UR is a notable contributor and pioneer in the area. The following topics have been elucidated: AI's application in managing the risk of AF complications is a hot mature topic; AI-electrocardiograph for AF diagnosis and AI-assisted catheter ablation surgery are the emerging and booming topics; smart wearables for real-time AF monitoring and AI for individualized AF medication are niche and well-developed topics. Conclusion: This study offers comprehensive analysis of the origin, current status, and future trends of AI applications in AF, aiming to advance the development of the field.
Indexed as
Identifiers
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.