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Page 291 Fuleihan et al. Art Int Surg 2024;4:288-95 https://dx.doi.org/10.20517/ais.2024.39

POSTOPERATIVE MONITORING AND REHABILITATION
AI continues to play a vital role in the postoperative phase, facilitating efficient recovery and optimizing
[36]
patient outcomes through solutions such as personalized rehabilitation plans . Lee et al. showed that an
AI-based real-time motion feedback system improved strength and engagement during rehabilitation in
[37]
spinal cord injury patients . Similarly, models have been applied to identify patients who may need
prolonged postoperative opioid prescriptions. Karhade et al. trained numerous models on a database of
5,413 patients and accurately predicted sustained postoperative opioid dependence between 90 and 180
[38]
days .

Leveraging longitudinal patient data, including clinical outcomes, activity levels, and patient-reported
measures, AI can predict the trajectory of spinal conditions as well as the risk of complications or disease
progression [39,40] . These predictive models can help identify high-risk patients, optimize surgical indications,
[41]
and guide proactive management strategies .
FUTURE FRONTIERS IN SPINE CARE DATA OPTIMIZATION AND ANNOTATION
With patients generating gigabytes of data, the sheer volume presents challenges to clinicians. AI
technologies can facilitate the interpretation of high-quality, structured data from diverse sources within the
[42]
clinical environment, making them readily available for further analysis . AI tools are also particularly
adept at extracting relevant data from large, unstructured datasets, a common challenge in medical settings.
When discussing the performance of AI models, metrics such as precision, recall, and specificity are vital for
evaluating their effectiveness in various tasks. These metrics help quantify how well an AI model identifies
relevant data and minimizes errors.

Additionally, AI significantly enhances dataset annotation by automatically labeling imaging datasets,
surgical videos, and other medical data with high accuracy . This capability accelerates the training process
[43]
for retrospective analyses, thereby increasing research efficiency and identifying areas for improvement in
the field.

Looking forward, the development of virtual scribes or “co-pilots” opens exciting possibilities. For patients,
AI-powered co-pilots can serve as personalized guides through the care continuum, providing education
and answering questions in real time. For surgeons, AI co-pilots can augment the surgical process by
[44]
providing robust decision support, analyzing intraoperative metrics, and suggesting surgical approaches .
We foresee a future where AI co-pilots integrate into the existing architecture of the spine surgery
ecosystem [Figure 1]. In addition to offering real-time information to surgeons, co-pilots could assist with
elevating critical non-technical roles, including improving communication, aiding with surgical team
efficiency, and maintaining situational awareness [45,46] .

CHALLENGES
While the implementation of AI in spine surgery holds immense promise, several challenges must be
addressed to fully realize its potential. A primary concern is the reliance on high-quality, standardized data.
High-quality data are essential for training accurate AI models, and standardization ensures that these
models can be applied broadly and effectively across different clinical settings [2,47] . With the paramount
importance of privacy and data security, compliance with regulations such as the Health Insurance
Portability and Accountability Act (HIPAA) of 1996 is necessary to safeguard patient information.