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Applications of Artificial Intelligence in Transcatheter Aortic Valve Replacement: A Review of the Literature
Flora Tsakirian1, Dimitrios Afendoulis1, Andreas Mavroudis1
1Unit of Structural and Valvular Diseases Heart Diseases, First Cardiology Department of Cardiology National Kapodestrian, University of Athens, General Hospital of Athens "Ippokratio", 11527 Athens, Greece.
Introduction:
Artificial 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.
Methods:
A 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].
Results:
According 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.
Conclusions:
AI 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.
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