ReviewJournal of cardiovascular development and disease2024
How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges.
Review in Journal of cardiovascular development and disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 20 citations in OpenAlex.
- A No-Code, Guideline-Based Custom GPT Outperforms Cardiologists in Response Quality for Cardiac Amyloidosis.JACC. Advances · 2026Article
- Artificial Intelligence, Connected Care, and Enabling Digital Health Technologies in Rare Diseases With a Focus on Lysosomal Storage Disorders: Scoping Review.Journal of medical Internet research · 2026Article
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- Bridging the Gap in AL and ATTR Cardiac Amyloidosis: Integrating Histopathology, Biomarkers, and Multimodal Imaging for Subtype-Specific Diagnosis.Reviews in cardiovascular medicine · 2026Review
- Transthyretin cardiac amyloidosis: advances and ambiguities.Heart failure reviews · 2025Review
- Tracking a patient with no technetium-99m-pyrophosphate myocardial uptake at surgical valve replacement for aortic stenosis.Journal of geriatric cardiology : JGC · 2025Article
- Challenges in Diagnosing Systemic Amyloidosis.JACC. Case reports · 2025Article
- Review
- Diagnosing AL and ATTR Amyloid Cardiomyopathy: A Multidisciplinary Approach.Journal of clinical medicine · 2024Review
- Review
- Chemotherapy Related Cardiotoxicity Evaluation-A Contemporary Review with a Focus on Cardiac Imaging.Journal of clinical medicine · 2024Review
- Artificial Intelligence Assists in the Early Identification of Cardiac Amyloidosis.Journal of personalized medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiac amyloidosis (CA) is an underdiagnosed form of infiltrative cardiomyopathy caused by abnormal amyloid fibrils deposited extracellularly in the myocardium and cardiac structures. There can be high variability in its clinical manifestations, and diagnosing CA requires expertise and often thorough evaluation; as such, the diagnosis of CA can be challenging and is often delayed. The application of artificial intelligence (AI) to different diagnostic modalities is rapidly expanding and transforming cardiovascular medicine. Advanced AI methods such as deep-learning convolutional neural networks (CNNs) may enhance the diagnostic process for CA by identifying patients at higher risk and potentially expediting the diagnosis of CA. In this review, we summarize the current state of AI applications to different diagnostic modalities used for the evaluation of CA, including their diagnostic and prognostic potential, and current challenges and limitations.
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.