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Updated: May 15, 2026
09:09
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Artificial Intelligence Application and Future Directions in Infiltrative and Inflammatory Cardiomyopathies: A
Gregorio Tersalvi1, Jose K James1, Zachi I Attia1
1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Journal of Cardiac Failure
|May 13, 2026
Summary
Artificial intelligence (AI) can analyze complex data to improve the diagnosis and prognosis of infiltrative and inflammatory cardiomyopathies. While promising, AI tools require further validation and integration into clinical practice.
Area of Science:
- Cardiology
- Medical Artificial Intelligence
- Biomedical Data Science
Background:
- Infiltrative and inflammatory cardiomyopathies present diagnostic and prognostic challenges due to nonspecific symptoms and limited tools.
- Current approaches may delay recognition and effective treatment, contributing to significant morbidity and mortality.
Purpose of the Study:
- To review the emerging applications of artificial intelligence (AI) in the detection, prognosis, and therapeutic guidance for cardiomyopathies.
- To discuss the potential of AI in uncovering complex patterns from diverse clinical data.
Main Methods:
- Review of recent literature on AI applications in cardiac amyloidosis, Fabry disease, myocardial iron overload, and inflammatory cardiomyopathies.
- Analysis of AI's role in interpreting clinical, imaging, biomarker, and histologic data.
Main Results:
- AI shows potential in identifying subtle patterns for early detection and improved prognosis in specific cardiomyopathies like cardiac amyloidosis.
- AI-enhanced electrocardiography and imaging may aid in the detection and management of inflammatory cardiomyopathies.
Conclusions:
- AI holds promise for complementing clinical judgment in managing cardiomyopathies by flagging subtle signals and guiding testing.
- Further research is needed to overcome limitations such as small sample sizes, retrospective designs, and generalizability issues before widespread adoption.
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