Critical Appraisal of Clinical Validation of Predictive Procedural Planning Software in a Real‐World TAVI Population

We read with great interest the article by Claes et al. [1], which focused on an artificial intelligence (AI)-guided procedural planning tool for transcatheter aortic valve replacement (TAVR). Strong internal validity was provided by the authors, and valve sizing prediction and assessment of paravalvular regurgitation (PVL) were performed well. Their results highlight increasing automation in structural heart procedures. However, some issues need to be further discussed for robust clinical translation. First, the comparison analysis of expert manual planning in terms of the quantity of shots does not exist. Validation only against procedural results means that you can't tell very much about whether the software is better than or just as good as expert judgment. Corbin et al. [2] recently highlighted that head-to-head validation with operator-guided procedural planning is still an essential component to verify reproducibility and detect algorithmic failure modes. Second, the lack of longitudinal outcomes such as valve durability, PVL persistence, and mortality limits the clinical relevance of model predictions. Khodaei et al. [3] showed that patient-tailored hemodynamic derangements of the kind elicited by PVL have a major impact on long-term prognosis. Associating predictive planning with these endpoints would provide stronger evidence of outcome relevance. Third, the effect of imaging heterogeneity and operator experience on the reproducibility of measurement was not studied. The CT acquisition settings and observer experience may influence AI-based segmentation. Evertz et al. [4] demonstrated marked variability in CT-based annular analysis, emphasizing that repeatability testing should be performed before clinical use. Fourth, workflow efficiency and cost-effectiveness need to be further developed despite the good potential of automation in the system. Toggweiler et al. [5] showed that while computer-aided software may offset preprocedural planning, effective organizational flow and operator training are key to efficiency. Usability statistics need to be assessed before broad adoption can be recommended. Finally, future studies should incorporate algorithm transparency and interpretability, as recent reviews highlight explicable AI as a prerequisite for regulatory approval and clinician trust. Without disclosure of model logic and validation procedures, integrating AI systems into patient care will remain challenging. In summary, Claes et al. represent a key initial validation of AI-guided TAVR planning. Before such tools can become part of standard practice, prospective multicenter comparison, reproducibility analysis, correlation with long-term outcomes, and evaluation of the workflow are required. The authors have nothing to report. The authors have nothing to report. The authors have nothing to report. The authors declare no conflicts of interest. The authors have nothing to report.