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Turlip et al. Art Int Surg 2024;4:324-30 https://dx.doi.org/10.20517/ais.2024.29 Page 326

The advent of AI-powered predictive modeling also holds immense promise in the realm of personalized
precision medicine. By assimilating vast repositories of patient data, including demographic information,
comorbidities, and procedural specifics, AI algorithms can generate prognostic models tailored to individual
patients, ushering in a new era where therapeutic decisions are guided by each patient’s unique physiology.
This is particularly important for patient risk stratification, where clinical variables can be used as inputs
(predictors) for the potential of operative complications. Pellisé et al. trained a random forest algorithm
with clinical variables from 1,612 patients with adult spinal deformity (ASD) and identified age, surgical
[12]
invasiveness, and deformity magnitude as potential risk factors for major complications . Predictive
models, such as random forest algorithms for complication risk stratification, undergo internal validation
through cross-validation and are, at times, externally validated using datasets from different clinical settings
to evaluate model transferability. In the study by Pellisé et al., internal validation was performed with an
80%/20% split between training/testing groups, measuring model performance through the observed area
[12]
under the receiver operating characteristic curve (AUC) and the Brier score . Ames et al. augmented this
approach by applying unsupervised hierarchical clustering to classify ASD based on patient demographics
and radiographic measurements with the goal of constructing a risk-benefit grid as a preoperative tool for
decision making .
[13]

Current work continues to build upon existing outcomes prediction and postoperative prognostication. ML
has been implemented to assess the likelihood of surgical site infection, major intra-operative
complications, hospital length of stay, or the necessity of blood transfusion after surgery [14-17] . This has led to
the development of universal prediction models trained retrospectively on large patient registries, such as
the American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) database.
The ACS-NSQIP developed an online calculator for morbidity and mortality risk, but reports demonstrated
poor predictive performance . Other groups have used the available ACS-NSQIP patient data as a resource
[18]
to train their own models, with early indications of clinical efficacy at predicting outcomes [19,20] . Fully
unsupervised models have extensive utility to revolutionize personalized care and stratify risk; however,
deploying under-validated AI tools can lead to inaccurate diagnoses or inappropriate treatment
recommendations, so caution is needed to ensure patient safety.

Lastly, an emerging implementation of ML and AI has been in the realm of outcomes assessment.
Traditionally, evaluation of surgical outcomes relies on physician interpretation of radiographic imaging
combined with patient questionnaires assessing changes in patient mobility, pain, and quality of life. These
patient-reported outcome measures (PROMs) offer valuable insight into patients’ own interpretation of
their health status and physical function. However, these methods contain inherent subjectivity and often
lack the precision and reliability needed for precise and actionable insights [21,22] . More recently, there has also
been a trend toward utilizing digital biomarkers and data-driven outcomes measurements in conjunction
with traditional PROMs. Objective measurements of patient mobility obtained from patient smartphones,
smartwatches, or other biometric wearables can add additional unbiased insight into patient function [23-26] .
The quantitative and continuous features of these data are well suited for integration with data-driven
statistical and ML techniques, and they have enabled surgeons to better quantify changes in patient mobility
after surgery and to predict which patients may be better suited to recover from a particular pathology [24,25] .

FUTURE DIRECTIONS
The use of accelerometer and GPS information is a relatively novel concept, and more complex ML
predictive models have yet to be applied. The incorporation of such models could significantly improve the
accuracy of patient assessments by providing real-time, continuous data that captures a patient’s functional
mobility in their everyday life. This can lead to a more detailed understanding of a patient’s functional