ArticleThe British journal of cardiology2025
Demystifying AI for early AF detection: enhancing diagnostic transparency across modalities.
Article in The British journal of cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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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.
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Authors and funding
3 authors.
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Abstract
This article explores using artificial intelligence (AI) to detect atrial fibrillation (AF) early, highlighting its potential to revolutionise cardiology. It reviews numerous studies demonstrating AI's superior accuracy to traditional methods, particularly in leveraging electrocardiography data from various sources like smart devices and chest radiographs. A key concern addressed is the 'black box' nature of some AI algorithms, emphasising the critical need for transparency to build clinician confidence and ensure ethical patient care. It concludes by advocating for policy changes and further research to enhance AI algorithm transparency and integration into clinical practice.
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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.