ARTICLE SUMMARY:
Building AI-driven computational approaches to solving challenges in spine surgery requires high-quality clinical data, which has been a challenge in spine. Efforts to determine which stenosis patients with grade 1 spondylolisthesis are appropriate for lumbar fusion are a case in point.
Capturing clinical data that provides high levels of evidence regarding spine treatments, including nonsurgical and surgical interventions, has been complicated, in part because diagnosis and causality are so heterogenous and often poorly understood. Efforts to sort out best practices for common procedures falter amid conflicting clinical trial results, the complexity of spine conditions, and lack of high-quality longitudinal databases. Yet, the need is urgent as costs rise, the population ages, and patients demand surgery to improve their quality of life.
