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can also provide personalized education materials, track patient-reported outcomes, and monitor adherence
to preoperative instructions, without requiring additional appointments or an advanced understanding of
medical terminology [22,23] . However, caution must be taken to ensure that patients are not being
misinformed by these systems.

Furthermore, AI systems, leveraging tools such as natural language processing, could also be integrated into
[25]
clinic settings to automate patient intake . Such “virtual scribes” can automatically generate
comprehensive clinical notes and summarize patient-provider interactions, reducing administrative burden.
Such safeguards include regularly updating the AI’s knowledge base to ensure it reflects the latest clinical
guidelines, implementing clear disclaimers that patients are interacting with a virtual assistant rather than a
clinician, and ensuring the AI defers to human oversight in complex or ambiguous cases.

Expanding the diagnostic armamentarium
Beyond the analysis of static imaging data, AI holds significant potential to enhance spinal diagnostics by
incorporating dynamic and longitudinal patient characteristics. Machine learning models and video capture
tools have been used to identify abnormal gait and compensation patterns and estimate biomechanical
variables, such as joint loading and range of motion, that are not easily discernible through traditional
clinical evaluation [26,27] . By integrating this dynamic data, clinicians can gain deeper insights into the
underlying causes of spinal disorders and tailor interventions accordingly.

INTRAOPERATIVE SPINE CARE
Navigation and surgical accuracy
AI technologies can significantly enhance the spine operative experience. For example, surgical navigation
systems powered by AI can seamlessly integrate with robotics to offer real-time guidance during complex
spinal procedures . While the regulatory burden remains high, this is due to the need for rigorous
[28]
validation to ensure patient safety and efficacy in high-stakes environments. The justification for adopting
these technologies lies in their potential to greatly improve surgical precision, reduce complications, and
enhance patient outcomes, which outweighs the hurdles posed by regulatory requirements. Future
applications of AI can enhance existing navigation systems, allowing for minimized intraoperative errors
[29]
and surgical risk via real-time corrections to unexpected changes .

AI can also simulate procedures for educational training and create individualized models based on patient
imaging. These models allow trainees to practice surgery on complex anatomical variations, serving as a
powerful training tool [30-33] . In addition to benefitting trainees and health professionals, these models help
patients better understand their conditions, facilitating more informed discussions about treatment options
and fostering engagement in the decision-making process.

Intraoperative documentation
In the operating room, AI techniques can streamline documentation. In plastic surgery, ChatGPT templates
have been shown to generate operative notes over 42 times faster than traditional methods . Furthermore,
[34]
with integration into billing and insurance information, AI can streamline the prior authorization process
by extracting and organizing patient information, reducing delays in patient care. Zaidat et al. demonstrated
a class-by-class accuracy between 77%-87% using a dataset of 902 operative notes of spine patients to
generate billing codes . While AI has made tremendous progress in improving administrative efficiency, it
[35]
still faces challenges with redundancy, inaccuracy, and hallucinations. Regular audits and human oversight
are essential to prevent these errors.