ReviewLife (Basel, Switzerland)2025
Applications of Artificial Intelligence in Transcatheter Aortic Valve Replacement: A Review of the Literature.
Review in Life (Basel, Switzerland), 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.
- Clinical Utility and Limitations of Traditional Risk Scores (EuroSCORE, EuroSCORE II, and STS-PROM) in Patients Undergoing TAVI: A Narrative Review.Journal of clinical medicine · 2026Review
- Discrepancy Between Invasive and Echocardiographic Transvalvular Gradients After TAVI Procedure: A Review of the Literature.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial intelligence (AI) tools have emerged in cardiovascular clinical practice. Regarding transcatheter aortic valve replacement/implantation (TAVR/TAVI) procedures, their utilization optimizes procedural planning, aids physicians with decision making, and predicts possible post-procedural complications. Moreover, machine-learning (ML) models, compared with traditional mortality risk scores, show promising results considering predicted mortality in TAVI patients. However, further validation is required. As the implementation of cardiovascular procedures can be challenging, AI technology broadens the armamentarium of tools that a clinician is able to use for a more comprehensive evaluation of patients, minimizing complications and resulting in optimum clinical outcomes.
methodsA comprehensive literature search was conducted through the PubMed and Google Scholar databases from inception to 20 September 2025, to identify relevant studies. The search strategy included the following keywords: ["TAVI" OR "TAVR"] AND ["AI", Artificial Intelligence].
resultsAccording to our database research, 7177 articles were initially screened, and 2145 duplicate articles were excluded. Eventually, 189 articles were evaluated by our reviewers and 51 articles of studies published between 2014 and 2025 were included in our review.
conclusionsAI algorithms could revolutionize the Heart Team decision making process, being not only a tool for patient evaluation but an active member of the team with applications to analyze and optimize all stages of the TAVI procedure, guide decision making and predict outcomes, and, with the contribution and evaluation of information from all human members of the team, enhance even more the patient-mediated medicine/interventions.
Indexed as
Identifiers
What Socratic 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 Socratic graph.