Key result
AI research in myocarditis expands markedly after 2021, dominated by CMR-based deep learning models.
Why the study?
Research on artificial intelligence applied to myocarditis is fragmented and lacks comprehensive synthesis despite growing interest and scientific output.
Research on AI applied to myocarditis is an emerging, highly fragmented field primarily driven by computer scientists using cardiovascular magnetic resonance data, highlighting a need for interdisciplinary clinical translation and global diversification.
Fragmented AI-myocarditis research, driven by computer scientists, should not yet change practice; leaves open needs for clinician-led validation and global diversification.
Background: Myocarditis remains a diagnostic challenge, and interest in using artificial intelligence to support noninvasive diagnostic evaluation has grown rapidly.Although the literature at the intersection of artificial intelligence and myocarditis has expanded substantially, it remains fragmented and lacks comprehensive synthesis.Objectives: To map the global scientific output on the application of artificial intelligence to myocarditis by identifying leading contributors, collaboration patterns, and emerging thematic trends.Methods: We conducted a bibliometric analysis of articles on artificial intelligence applied to myocarditis indexed in the Web of Science Core Collection.The R package bibliometrix and VOSviewer were used to perform bibliometric network and thematic analyses.Results: A total of 47 articles were included.Scientific output increased markedly after 2021, coinciding with the COVID-19 pandemic, and peaked in 2025.China led in publication volume, whereas the United Kingdom and Germany achieved the greatest citation impact.No publications originated from Latin America, the Caribbean, or Africa.International collaboration networks remain fragmented.Keyword and thematic analyses identified the integration of deep learning with cardiovascular magnetic resonance imaging as the principal motor theme, reflecting a shift away from traditional endomyocardial biopsy.The most highly cited authors were primarily computer and data scientists.Conclusions: Research at the intersection of artificial intelligence and myocarditis is an emerging field undergoing methodological consolidation, with growth accelerated by the COVID-19 pandemic.Cardiovascular magnetic resonance imaging has become the primary data source for artificial intelligence-based applications.The principal challenge appears to be translational, underscoring the need for stronger collaboration between algorithm developers and clinicians.The successful integration of these models into routine clinical practice will likely depend on multidisciplinary collaboration and validation across diverse, multicenter populations.
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Costa et al. (2026) studied Myocarditis (n=47). Artificial Intelligence was evaluated on Global scientific output, collaboration patterns, and thematic trends. Research on artificial intelligence in myocarditis expanded markedly after 2021, with cardiovascular magnetic resonance imaging emerging as the primary data source for deep learning models.
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