ArticleHeart rhythm O22025
Leveraging artificial intelligence for risk stratification of inherited cardiomyopathies in under-resourced settings.
Article in Heart rhythm O2, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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.
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Who cites it
1 citing paper in PubMed.
- Can artificial intelligence transform cardiomyopathy diagnosis and risk stratification in the Philippines?Heart rhythm O2 · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inherited cardiomyopathies are a significant global cause of sudden cardiac death, particularly among younger individuals and those in under-resourced regions. Despite progress in diagnostics and therapeutics, screening and risk stratification remain challenging due to genetic complexity, variable clinical presentation, and the interpretive limitations of current electrophysiological and imaging tools. Artificial intelligence (AI)-particularly machine learning, deep learning, and natural language processing offers transformative potential by enabling large-scale analysis of complex data and detecting subtle disease patterns which could potentially improve diagnostic accuracy and cost-effectiveness, particularly in low-resource environments. This review evaluates the limitations of existing risk models, synthesizes disease-specific AI applications within a unified framework, and explores the role of AI in advancing personalized care and risk prediction in underserved populations.
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Registered trials
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