ReviewAnnals of medicine and surgery (2012)2025
Artificial intelligence in interventional cardiology: a review of its role in diagnosis, decision-making, and procedural precision.
Review in Annals of medicine and surgery (2012), 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.
- Explainable and Trustworthy Artificial Intelligence in Cardiology: A Narrative Review of Clinical Applications, Operational Integration, and Future Directions.Journal of clinical medicine · 2026Review
- Unveiling patterns in clinical data: exploring the role of large language models and clustering algorithms.Frontiers in artificial intelligence · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular diseases significantly burden healthcare systems globally, necessitating innovative solutions to enhance diagnosis, treatment, and patient management. Artificial intelligence (AI) is no longer a distant promise in interventional cardiology but a rapidly emerging tool with growing clinical impact. AI-driven technologies can analyze vast amounts of clinical data, recognize intricate patterns, and generate clinically relevant, evidence-based recommendations, augmenting physician expertise and streamlining care. In diagnostics, AI enhances imaging interpretation and lesion assessment, while procedurally, it supports real-time guidance and catheter-based interventions. Its integration into decision support systems has improved risk stratification, early disease detection, and individualized treatment planning. AI also advances personalized medicine using predictive models to tailor interventions to patient-specific needs. Despite its promise, challenges such as costs, ethical issues, and the need for rigorous validation remain barriers to widespread adoption. Nevertheless, as AI advances, its integration into interventional cardiology is expected to transform care delivery, optimize outcomes, and improve system efficiency.
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.