Innovation in aortic surgery requires more than AI: a perspective on data governance and shared decision-making

Abstract

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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