Abstract
The pace of US Food and Drug Administration-approved medical devices that incorporate artificial intelligence (AI) or machine learning as part of the device is accelerating. As of September 2021, 350 such devices have been approved for commercial sale in the United States. As much as AI has become ubiquitous in our lives-keeping our cars between the lines on the highway, converting speech to text on the fly, recommending movies, books, or restaurants, and so much more, AI also appears destined to become a routine aspect in daily spine surgery. Neural network types of AI programs have achieved extraordinary pattern recognition and predictive abilities-far surpassing human capabilities-and thus appears well suited to back pain and spine surgery diagnostic and treatment pattern recognition and prediction tasks. These AI programs are also data hungry. As luck would have it, surgery generates an estimated 80 MB per patient per day collected in a variety of datasets. When aggregated, this represents a 200+ billion patient record data ocean of diagnostic and treatment patterns. Such Big Data, when combined with a new generation of convolutional neural network (CNN) AI, set the stage for a cognitive revolution in spine surgery. However, there are important issues and concerns. Spine surgery is a mission-critical task. Because AI programs lack explainability and are absolutely reliant on correlative, not causative, data relationships, the emerging role of AI and Big Data in spine surgery will likely come first in productivity tools and later in narrowly defined spine surgery tasks. The purpose of this article is to review the emergence of AI in spine surgery applications and examine spine surgery heuristics and "expert" decision models within the context of AI and Big Data.