Can artificial intelligence in spine imaging affect current practice? Practical developments and their clinical...

As artificial intelligence (AI) increases its footprint in spine imaging, gauging the clinical relevance of new developments poses an increasingly difficult challenge, especially given the majority of developments reflect experimental or early work. With this summary of available AI tools, focusing on those in clinical use, the benefits of AI in spine imaging are explained for radiologists and surgeons to understand the current state of and potentially the decision to adopt AI in clinical practice.

Through a narrative review of publications relating to "artificial intelligence" and "spine imaging" in the PubMed database, this article provides an update on AI applications in spine imaging being utilized in current clinical practice.

Current applications of AI in spine imaging range from deep learning image reconstruction and denoising, spine segmentation and biometry, radiological report generation, surgical outcomes prediction, surgical planning, to intraoperative assistance. Developments in deep learning reconstruction (DLR) are most mature and demonstrate improvements to imaging speed and interpretability compared to non-AI alternatives. While clinical implementations exist in other use cases, their performance remains either an area of active investigation or comparable to the level of a human.

Uses of AI in spine imaging span multiple applications with early clinical implementation in most areas, suggesting a promising future ahead.