Artificial intelligence in spine care: a paradigm shift in diagnosis, surgery, and rehabilitation

Artificial intelligence (AI) has emerged as a transformative force in modern medicine, offering innovative solutions to longstanding challenges in spine care. From enhancing diagnostic precision to optimizing surgical workflow and potentially improving patient outcomes, AI’s applications are reshaping the management of spine care. However, the successful implementation of AI is contingent on the availability of high-quality, evidence-based data that ensures the accuracy and reliability of its algorithms. As AI continues to permeate the field of spine care, this article explores its role across the continuum of patient management, surgical process and flow, and long-term follow-up, supported by recent advancements in the field (1).

Figure 1 illustrates the publication trends in the field of AI applied to spine surgery from 1994 to 2024. Initially, there were minimal publications, with negligible activity until the early 2000s. A sharp and significant rise is evident post-2018, with exponential growth in publications from 2019 onward, indicating an accelerated adoption and research focus on AI technologies in this specialized field. By 2024, the number of publications reaches over 300, showcasing the rapid expansion and importance of AI in advancing spine surgery practices and outcomes (Figure 1).

Figure 1 PubMed data on “artificial intelligence” and “spine surgery” demonstrates a remarkable rise in the number of publications over recent years. These publication volumes will only continue to accelerate. AI, artificial intelligence.

The journey of spine care begins with a patient’s presentation of symptoms such as pain, or neurological deficit. Early and accurate diagnosis is critical to decision making and improving outcomes. AI-powered tools, including chatbot-based symptom checkers and predictive models, are enabling more precise risk stratification and early detection of spine pathologies (Figure 2). These tools analyze patient-reported symptoms in conjunction with clinical and demographic data, offering preliminary evaluations that guide clinicians in prioritizing diagnostic investigations. Recent advancements suggest that such applications can enhance accessibility to specialist-level assessments, particularly in underserved regions (2,3).

Figure 2 AI has an expanding role in every aspect of the clinical workflow of the spine patient. AI, artificial intelligence; MRI, magnetic resonance imaging; CT, computed tomography; AR, augmented reality; apps, applications.

The diagnostic phase often advances to imaging studies, where AI has made significant inroads. Radiology, a cornerstone of spine care, benefits immensely from AI’s capacity to process complex imaging data. AI algorithms excel in analyzing modalities such as magnetic resonance imaging (MRI), computed tomography (CT), and X-rays, identifying abnormalities such as vertebral fractures, disc degeneration, and neural compression with exceptional accuracy (3,4). Furthermore, these tools can segment spinal structures and highlight anatomical variations, aiding clinicians in surgical planning. Automated systems for calculating spinal parameters, such as the Cobb angle in scoliosis, are streamlining workflows and enhancing diagnostic consistency (3). By integrating large datasets from imaging repositories, AI offers tailored insights, allowing surgeons to develop precise, patient-specific interventions (4).

AI’s transformative potential extends into the operating room, where it enhances surgical workflows and intraoperative decision-making. Robotic systems, integrated with AI, have the potential to enable sub-millimeter accuracy in instrument placement during complex spine surgeries such as pedicle screw fixation. Augmented reality (AR), another AI-driven innovation, overlays real-time, three-dimensional visualizations of patient anatomy onto the surgical field, assisting surgeons in navigating challenging anatomical landscapes. Many such devices are currently in use with relevant regulatory approvals. These tools reduce reliance on manual estimation, enhancing precision and minimizing complications. Moreover, intraoperative AI systems may monitor critical parameters and provide real-time alerts, enabling dynamic decision-making that mitigates risks. This fusion of human expertise and machine intelligence is revolutionizing spine surgery by improving safety and outcomes (4,5).

The postoperative phase presents its own set of challenges, particularly in the detection of complications. AI-powered imaging algorithms play a pivotal role in identifying issues such as hardware malposition, infection, and delayed healing. By analyzing subtle changes in post procedural imaging data, these tools enable early interventions that prevent adverse outcomes. Beyond imaging, wearable devices equipped with AI capabilities are becoming essential in rehabilitation. These devices monitor metrics such as mobility, muscle activation, and adherence to prescribed exercises, offering real-time feedback to both patients and healthcare providers. AI-driven video analysis further refines rehabilitation protocols, ensuring exercises are performed correctly and effectively. This personalized approach accelerates recovery and empowers patients in their journey toward optimal functionality (5,6).

Long-term monitoring of spine patients is another domain where AI is making significant strides. Wearable technology, smartphone applications, and remote monitoring platforms are facilitating continuous tracking of health metrics such as gait function, pain levels, sleep quality, and activity patterns. AI algorithms analyze these data streams to detect early signs of recurrence or deterioration, enabling timely interventions. For patients with chronic spine conditions, these tools offer a proactive approach to care, reducing hospital readmissions and improving quality of life. The integration of these technologies ensures that care is not only continuous but also personalized to the evolving needs of the patient (7,8).

Despite these advancements, the implementation of AI in spine care is not without challenges. The reliability of AI systems is inherently tied to the quality of the data used to train them. High-quality, multicenter clinical studies are essential to ensure that AI algorithms are accurate, unbiased, and generalizable across diverse populations. Collaboration between clinicians, data scientists, and policymakers is crucial to developing ethical frameworks and ensuring transparency in AI applications. By addressing these challenges, the spine care community can harness AI’s full potential while safeguarding patient safety and efficacy.

AI’s integration into spine care represents a fundamental shift in how conditions are diagnosed, treated, and managed (9). By bridging technological innovation with clinical expertise, AI is poised to redefine standards of care, enhance patient outcomes, and streamline healthcare delivery. As research and validation efforts continue, the promise of AI in spine care will undoubtedly expand, offering unprecedented opportunities for transformation.

Acknowledgments

Funding: None.

Footnote

Provenance and Peer Review: This article was a standard submission to the journal. The article has undergone external peer review.

Peer Review File: Available at https://jss.amegroups.com/article/view/10.21037/jss-24-156/prf

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://jss.amegroups.com/article/view/10.21037/jss-24-156/coif). R.J.M. serves as the Editor-in-Chief of Journal of Spine Surgery. R.J.M. provides consultancy services to Elliquence for training junior surgeons. The author has no other conflicts of interest to declare.

Ethical Statement: The author is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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