Key result
Artificial intelligence and predictive modeling in aortic surgery require robust data governance and shared decision-making to ensure innovations are scientifically sound and responsive to patient needs.
Advancing decision-making in aortic surgery requires deliberate investment in building data ecosystems and fostering shared decision-making communication practices alongside AI innovations.
Clinical decision-making for individuals undergoing valvular and aortic surgery remains challenging, particularly in young patients facing lifelong risk and repeated interventions. As predictive technologies such as artificial intelligence and advanced statistical modelling evolve, the surgical community must ensure that key foundational elements, namely data governance, including data standardization, and regulation, are firmly in place. Without high-quality, standardized, and ethically governed data, predictive models risk offering misleading guidance rather than meaningful personalization. While data governance ensures the scientific robustness of predictive technologies, shared decision-making (SDM) ensures these innovations remain closely aligned with the lived experiences, values, and preferences of individual patients. This perspective emphasizes that advancing decision-making in aortic surgery requires deliberate investment in building data ecosystems and fostering SDM communication practices, ensuring that innovation is both scientifically sound and truly responsive to patient needs.
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Keijzer et al. (2026) conducted an editorial in Aortic and valvular disease. Artificial intelligence and shared decision-making was evaluated. Artificial intelligence and predictive modeling in aortic surgery require robust data governance and shared decision-making to ensure innovations are scientifically sound and responsive to patient needs.
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