ReviewReviews in cardiovascular medicine2026
Advancements and Applications of Artificial Intelligence in Hypertrophic Cardiomyopathy: A Comprehensive Review.
Review in Reviews in cardiovascular medicine, 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
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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.
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
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypertrophic cardiomyopathy (HCM) is a common cardiovascular disease and one of the leading causes of exercise-induced sudden cardiac death in adolescents. HCM presents complex diagnostic, prognostic, and management challenges due to the phenotypic heterogeneity and clinical course. Artificial intelligence (AI), machine learning (ML), and deep learning (DL) technologies are expected to transform the roles of echocardiography, electrocardiography (ECG), and cardiac magnetic resonance (CMR) imaging in the clinical management of HCM. AI methods can fully integrate clinical and imaging data to enable a comprehensive assessment of the risk profile of a patient. However, challenges remain, such as insufficient data standardization across multiple sources, limited model interpretability, and data privacy issues. Despite these challenges, AI-based approaches have the potential to revolutionize the management of HCM by providing timely, accurate diagnoses and personalized treatment strategies based on individual patient risk profiles. This review systematically examines the current landscape of AI applications in HCM data analytics, with a focus on methodological advancements and clinical implementations. Furthermore, this review aims to facilitate the transition from experience-based to data-driven paradigms in HCM diagnosis, thereby advancing precision medicine and individualized patient management.
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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.