## 7 Conclusions  
By migrating the post-TAVR complication prediction platform to FL post-TAVR, this study presented a methodology for moving previous centralized medical AI solutions into a federated learning environment. This methodology demonstrates that traditional centralized AI solutions can perform adequately in a federated learning setting, indicating that AI solutions involving multiple institutions can be applied to various fields where data privacy is a major concern. This solution enables the safe and practical use of large datasets held by different institutions, demonstrating that high-performance models can be developed collaboratively without sharing sensitive data. The nonexistence of a performance disparity between centralized and federated learning methodologies signifies that federated learning, equipped with its lifecycle administration framework, facilitates enduring model performance augmentation and governance. This implies that, over an extended period, federated learning possesses superior prospects for progression in comparison to conventional centralized techniques.  
Using this methodology, it is feasible to deploy rapid and consistent federated-learning AI solutions within the medical domain. Although this study focused on TAVR as a case study, the proposed methodology applies to diverse medical domains beyond TAVR. AI solutions that possess both scalability and reproducibility can significantly advance innovation across various fields such as diagnosis, treatment, and prediction. These solutions serve as the foundation for consumable federated learning, ultimately facilitating the formation and growth of a market that consumes federated learning solutions. This strategy surpasses the constraints of conventional techniques that necessitate centralized data collation, introducing a continuous federated learning lifecycle methodology that facilitates the ongoing enhancement of AI models without mandating data dissemination among organizations.  
Consequently, this case of transitioning centralized AI solutions to a practical federated learning-based approach can serve as a guide for converting existing AI solutions in the medical sector into continuous federated learning environments. This provides a methodology for operating multi-institutional AI solutions while ensuring data privacy, thereby contributing to the expansion of the practical applicability of AI solutions involving multiple institutions in the medical domain, and it is anticipated to aid in the progression of a global and perpetually continuous AI resolution. This federated-learning-based approach will be established as a general methodology for applying AI models to the utilization of medical data, and it shall represent a resolution that perpetually develops throughout its functionality. Based on the findings of this study, it is anticipated that future research on the application of federated learning-based artificial intelligence will be actively conducted across various medical domains.  
Design and writing, M.H.J. and I.S.; conceptualization, M.H.J., I.S. and K.L.; experiment, M.H.J. and I.S.; writing--original draft preparation, K.L.; supervision and contribution, K.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (grant number HI22C1651).  
The need for consent was waived by the institutional review board of Gachon University Gil Medical Center (ethics approval code: GAIRB 2022-345).  
Data Availability Statement: The data presented in this study are not publicly available due to ethical restrictions and cannot be shared.  
The authors declare no conflicts of interest.