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Page 289 Fuleihan et al. Art Int Surg 2024;4:288-95 https://dx.doi.org/10.20517/ais.2024.39

INTRODUCTION
Artificial intelligence (AI) is increasingly being adapted for healthcare purposes, including analyzing
[1,2]
complex data, identifying patterns, and making predictions and decisions [Supplementary Table 1] . A
new frontier in AI has emerged with the genesis of generative AI models, which can create new content,
including text and images, based on input data. These models could be leveraged to generate tailored
surgical plans, create patient education packets, and assist in clinical documentation. Estimates project that
AI solutions could potentially save up to $360 billion dollars of US healthcare expenditure (5%-10%)
[3]
annually . Here, we provide a comprehensive perspective on the existing applications as well as the
frontiers and challenges for AI integration in spine surgery, including preoperative planning, intraoperative
care, and postoperative management.

PREOPERATIVE PLANNING AND PATIENT SELECTION
Imaging analysis
AI algorithms excel at accurately analyzing spine imaging data, enabling the detection and characterization
of pathologies with remarkable precision. For example, deep learning models have been developed for the
automated detection of vertebral compression fractures on computed tomography (CT) or magnetic
[4]
resonance imaging (MRI) scans . Al Arif et al. used a training set of 138 X-rays and a test set of 172 images
to identify vertebral centers and outlines with an average error of only 1.81 and 1.69 mm, respectively .
[5]
Doerr et al. used a region-based convolutional neural network to train and validate a deep learning model
that can predict and classify a patient’s thoracolumbar trauma based on CT imaging alone, reducing the
[6]
need to pursue additional costly and time-consuming MRI imaging for assessment . AI can thereby help
[7]
rapidly identify and triage patients in emergent settings and expedite the time to surgical intervention .
Risk stratification and surgical planning
Machine learning algorithms can predict the risk of complications, such as surgical site infections, venous
thromboembolism, and reoperation, during and after spinal procedures [8-13] . Pellisé et al. utilized data from
1,612 patients across two independent prospective databases on adult spinal deformity to develop
prognostic models for major complications, readmissions, and reoperations. The models can be used
preoperatively to identify patients at greatest risk of postoperative complications and improve the patient
counseling process .
[14]

While AI is still in its early stages, it has shown significant potential when trained on robust and extensive
retrospective data. For example, machine learning algorithms have shown that they can outperform
surgeons’ gestalt in predicting the risk of complications after emergency general surgery, including
mortality, bleeding, and pneumonia . AI-driven predictive risk models can also incorporate
[15]
biopsychosocial patient factors including demographics, comorbidities, frailty, laboratory values, and
imaging data, as well as surgical details including approach, spinal levels, and instrumentation - all of which
are critical cues in spine surgery [16-18] . For instance, Goedmakers et al. developed a deep learning algorithm
to predict adjacent segment disease following anterior cervical discectomy and fusion surgery, using only
preoperative cervical MRI scans. The algorithm achieved a 95% accuracy rate, significantly outperforming
expert neurosurgeons and neuroradiologists, who achieved only 58% accuracy .
[19]
Patient engagement and education
Large language models can simplify the reading levels of consent forms from a collegiate level to a seventh-
grade level, allowing for more patient accessibility and understanding [20,21] . Moreover, AI-powered virtual
assistants and chatbots can be harnessed as valuable care companions capable of providing knowledge to
patients on demand [22,23] . Boczar et al. created an AI-powered virtual assistant that correctly answered 92% of
patient questions regarding plastic surgery in a sample of 30 participants and 294 questions . AI systems
[24]