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Page 327 Turlip et al. Art Int Surg 2024;4:324-30 https://dx.doi.org/10.20517/ais.2024.29

baseline status and postoperative recovery, resulting in tailored personalized medicine. While many analyses
of mobility data have been retrospective in nature, upon the growth of adequate datasets, predictive models
may be able to accurately identify subtle changes in mobility-related complications or improvements earlier
than would be possible with traditional assessments.

Further, advanced mobility metrics can add potential value for patient prognostication. As previously
mentioned, groups are beginning to engineer universal prognostic models for outcome prediction trained
on large data registries [19,20] . Although still in their infancy, accurate prognostic models could transform
patient management by offering more realistic recovery trajectories, customizing patient care, or identifying
high risk for adverse outcomes. There are still challenges that limit the widespread implementation of such
models, ranging from access to generalizable datasets, cost-effectiveness for stable implementation, or
ethical concerns.

Mobility metrics are not the only AI application that is challenged with limited data availability. Access to
high-quality, standardized data sets is one of the greatest challenges to overall AI and ML implementation,
especially within spine surgery, given the varied and nuanced model inputs spanning complex patient
presentations, operative courses, and radiographic imaging. To address this challenge, there is a growing
movement toward the creation of standardized, multi-center datasets that include patients from several
geographic areas and socioeconomic groups. Other groups such as the ACS are refining their existing
patient registries to integrate additional data from the electronic health record. Together, these datasets and
registries aim to provide a foundation for training more accurate and generalizable AI models that can be
deployed across various clinical settings.

Patient selection is another area of current clinical practice that stands to benefit from future AI and ML
integration. The art of understanding which patients will benefit from certain procedures is not easily
replicated with frameworks and rules that can be directly input into computerized programs. However, as
CNNs and ML algorithms continue to grow in computational ability, they can potentially identify
relationships between datapoints that are otherwise unnoticeable to the un-aided human mind; in this way,
future AI and ML models can augment surgeons’ clinical practice and assist in identifying certain patient
characteristics that are indicative of patients likely to benefit from specific surgical interventions.

While AI technologies like predictive modeling and image analysis hold promise in decision making, their
[1,7]
potential intra-operative impact is already apparent . AI-assisted intra-operative tools, such as robotics,
navigation systems, and mixed reality, have the potential to significantly enhance the surgeon’s ability to
execute procedures with high precision, particularly in minimally invasive and percutaneous surgeries.
These technologies allow for real-time guidance and adjustment during complex procedures, reducing the
margin of error. However, while AI can minimize the risk of intra-operative errors, it cannot fully replace
the human element of adaptability and judgment. Surgeons must remain vigilant in managing unforeseen
intra-operative variables and complications, as AI systems, though highly advanced, still require human
oversight to ensure patient safety and the proper handling of unexpected challenges.

Although surgeon experience is regarded as a significant factor in decision making, there have been
[27]
attempts to apply mathematical and data-driven approaches to surgical decision making . Lewandrowski
et al. recently used the Rasch model to determine the choice of procedure for endoscopic lumbar
[27]
decompression . The Rasch model is a logistic function analyzing categorical data, such as questionnaire
responses, to find the relative difficulty of a task, and it has been widely established in education, marketing,
and health economics . However, it was found that there was still disagreement among surgeons regarding
[28]