ReviewEuropean cardiology2026
Application of Artificial Intelligence in the Early Detection and Management of Atrial Fibrillation: State-of-the-art Review.
Review in European cardiology, 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
AF is the most common cardiac arrhythmia in clinical practice, with a significant impact on morbidity, mortality and healthcare costs. Optimal management of AF requires a multidimensional approach that includes early and accurate diagnosis, the choice between rate and rhythm control strategies and the integrated management of associated comorbidities. In the age of artificial intelligence (AI), a new paradigm in AF care is emerging, thanks to innovative tools capable of supporting clinicians throughout all phases of the diagnostic and therapeutic journey. AI-based algorithms can improve diagnostic accuracy through the analysis of standard ECGs or wearable devices, predict arrhythmic events or complications and guide personalised therapeutic decisions. Furthermore, the integration of AI into healthcare systems enables more efficient management of comorbidities, promoting a holistic and proactive approach. This review explores the potential of, and challenges involved in, using AI in the management of AF, outlining a future scenario in which the technology can amplify clinical expertise and improve patient outcomes.
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Registered trials
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