ReviewCureus2025
The Role of Artificial Intelligence in the Detection of Cardiac Amyloidosis: A Systematic Review.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
2 citing papers in PubMed.
- Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine Learning Application.Cardiology research and practice · 2026Review
- Advances in imaging-based diagnosis, prognosis, and response assessment in cardiac amyloidosis: a comprehensive multimodality review.Annals of nuclear medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiac amyloidosis (CA) is an underdiagnosed condition that occurs when misfolded amyloid proteins deposit in cardiac tissue causing progressive myocardial dysfunction. Artificial intelligence (AI) technology demonstrates strong potential to enhance the identification of various cardiovascular diseases, including CA. This systematic review examines how AI systems identify CA. This study explores AI's application as a diagnostic tool for CA. We searched five databases (PubMed/MEDLINE, Scopus, Web of Science, Embase, and IEEE Xplore) for relevant studies using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Ten of the 629 studies we discovered were determined to be suitable for the current systematic review. We used the PICOS (P: population, I: intervention or exposure, C: comparison, O: outcome, S: study type) framework to collect relevant data from the included studies. The screening procedure was blinded and used predefined inclusion and exclusion criteria. It was undertaken in two phases. Whenever disagreements emerged, the reviewers deliberated and concluded. After the screening procedure, 10 studies in all were judged suitable for this review. Through the analysis of standard laboratory data, transthoracic echocardiography, electrocardiogram, medical records, cardiac magnetic resonance, and whole-body scintigraphy, these studies assessed the potential usefulness of AI models in diagnosing CA. AI models have been useful as a CA diagnostic tool, outperforming professional cardiologists in one instance or being on par with them in others.
Indexed as
Identifiers
What OpenQuestion holds
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.