After years of both increasing enthusiasm and skepticism by surgeons and patients, the first artificial intelligence (AI) and machine learning (ML) algorithms for treating patients with aortic disease are now maturing. Multiple AI- and ML-enabled tools in this space are now FDA approved, being commercialized, and, in turn, having an increasing impact across the care continuum. They are being used to evaluate radiologic imaging in order to increase the speed and accuracy of diagnoses including aortic dissection and aneurysm. Other tools are assisting with surgical planning, intraoperative guidance, decreasing radiation exposure, and facilitating or performing post-operative surveillance.
These technologies are continuing to mature and their integration with the healthcare system is expected to accelerate in the coming years as algorithm performance improves, regulatory framework solidifies, and hospital systems, payors, and physicians recognize their utility. Although barriers still exist, as these algorithms and their enabled tools become integrated with other emerging technologies, the rapidity at which new products mature and become useful will increase. The goal of this article is to review the major themes in mature AI and ML algorithms in aortic surgery with a focus on image analysis, diagnosis, and open and endovascular surgical planning and intraoperative guidance. We authored and revised this article about AI and ML in the aortic surgery space with help of a natural language AI-enabled tool called ChatGPT. This highlights just one example of how technologies from completely unrelated spaces will accelerate progress in vascular surgery in unpredictable ways.