Artificial Intelligence in Spine Surgery Practice

The revolutionary power of artificial intelligence (AI) is making its way into every corner of healthcare, and spine surgery is no exception. From patient education to surgical planning, AI is being seamlessly integrated into various roles to improve patient outcomes.1 Historically, technological milestones in spine surgery have centered on imaging techniques and instrumentation, such as 3D fluoroscopy, intraoperative CT/ MRI, and cervical pedicle screws.2 Now, AI is ushering in a new era defined by mechanical precision and cognitive augmentation. Large language models (LLMs) such as ChatGPT are increasingly being adopted into clinical environments to reduce human error and support decision-making based on findings from a wide-net of medical literature.3 By providing evidence-based insights within seconds, these tools allow clinical staff to dedicate more time to direct patient care and hands-on responsibilities. In the operating room, emerging technologies such as the Apple Vision Pro are enhancing real-time anatomical visualization, showing promising strides in advancing minimally invasive spine surgery (MISS).4 As AI continues to evolve, examining its current applications in spine surgery can help inform surgeons of efficacious ways to further improve surgical performance and the perioperative patient experience.

AI Tools and Technological Innovations

Large Language Models

As previously mentioned, LLMs are emerging versatile tools within spine surgery. Recent studies have demonstrated the growing utility of LLMs in enhancing productivity and decision-making. For example, LLMs can facilitate case discussions, surgical planning, and patient communication.5 Although they are less accurate than trained surgeons in nuanced decision-making, LLMs can still reasonably interpret imaging findings and provide treatment recommendations.6 Bard, another commonly used LLM, can generate informative responses to frequently asked questions about lumbar spine fusion. When compared to ChatGPT, both LLMs provided satisfactory to excellent responses 97% of the time.7 Their shortcomings were most evident when answering questions regarding surgical risks, success rates, and selection of surgical approaches. Although AI may appear to lack human empathy, both models scored well on empathy and understanding.

Beyond clinical application, LLMs are being explored for their potential in research and documentation. They can help reduce the burden of time-consuming academic tasks such as drafting literature reviews, generating research hypotheses, and designing tables.3 Furthermore, accessible chatbots such as ChatGPT, Bard, and Bing AI were tested on their knowledge of orthopedic concepts and clinical management.8 ChatGPT notably outperformed Google Bard and BingAI in topics such as bone physiology, providing next steps in diagnosis or management, and patient inquiries. For instance, ChatGPT answered questions pertaining to bone physiology correctly 83% of the time, compared to BingAI’s 23%. Considering the rate of inaccuracies in widely accessible LLMs such as ChatGPT, Google Bard and BingAI, it is crucial that patients who seek convenient answers are made aware of the potentially misleading and poorly referenced suggestions they may receive from these chatbots. Researchers caution against overreliance on LLMs, highlighting that these models are not exempt from referencing unreliable sources (eg, social media posts, news articles, and Wikipedia).9

Immersive Visualization Tools

Immersive technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR) are expanding possibilities in spine surgery. Head-mounted displays such as the Apple Vision Pro provide real-time 3D anatomical overlays, enhancing spatial awareness and precision in the operating room. A growing body of evidence supports their utility in improving surgical workflow and reducing reliance on fluoroscopy. In a cadaveric study, it was found that MR-based navigation allowed for pedicle screw placement with accuracy comparable to traditional techniques while offering the benefit of a heads-up display that keeps the surgeon’s focus within the operative field.10 Similarly, in a systematic review of AR/MR-enhanced spine procedures, improvements in operative accuracy, reduced radiation exposure, and shortened learning curves across multiple spinal interventions were observed.11 Furthermore, these immersive platforms serve as valuable tools for preoperative rehearsal and surgical training. VR and AR modules are increasingly used to simulate complex procedures, enabling surgeons to refine their technique and anatomical familiarity without risk to patients.12 These tools also hold promise in enhancing patient education, allowing individuals to better visualize their pathology and proposed interventions. As the integration of AI continues, future developments may enable these systems to adapt in real time based on intraoperative imaging or predictive modeling, further personalizing and optimizing spine surgery.

Machine Learning

Machine learning (ML) has shown remarkable promise in the field of spine surgery by enabling predictive analytics, risk stratification, and personalized treatment planning. Clinicians can input complex datasets such as patient demographics, imaging, surgical variables, and outcomes to generate models that support clinical decision-making and outcome forecasting.13

Recent studies have demonstrated the utility of ML in preoperative risk assessment and postoperative outcome prediction. One study developed ML models that accurately predicted postoperative disability and pain following lumbar disc herniation surgery.14 Thus, these models demonstrate high potential to tailor interventions and guide patient counseling. Furthermore, ML applications in degenerative spinal conditions have been shown to have consistent success in predicting surgical outcomes, complications, and hospital readmissions.15 These capabilities can be particularly valuable in identifying high-risk patients, optimizing resource allocation, and setting realistic expectations for recovery.

Beyond outcome prediction, ML is also being applied to diagnostic support and clinical workflow optimization. ML algorithms have been trained to detect spinal pathologies on imaging and assist in the classification of degenerative conditions, often with accuracy comparable to or exceeding that of experienced clinicians.13 Further emphasis has been placed on the expanding role of ML in intraoperative planning, where real-time data integration can support decisions such as optimal implant selection and trajectory guidance.16 While the majority of these tools remain in the research phase, systematic reviews affirm that ML has reached a stage where its integration into clinical practice is both feasible and increasingly supported by evidence.15

Computer Vision and Automated Imaging

Computer vision, a discipline within artificial intelligence focused on enabling machines to interpret and process visual data, is revolutionizing diagnostic imaging and intraoperative navigation in spine surgery by automating tasks such as spinal segmentation, alignment analysis, and pathology detection.17 Another use of AI algorithms is to classify low back pain on MRI scans, enhancing early detection and treatment planning for degenerative conditions. In the operating room, machine vision tools have begun replacing traditional fluoroscopy for intraoperative guidance. A recent study found that a novel image-guided system significantly reduced both procedural time and radiation exposure during pediatric spinal deformity surgery.18 These advancements reflect a growing shift toward real-time, data-enhanced decision-making in surgery, with the potential to improve safety and streamline workflows.

Pitfalls of AI

The far-reaching capabilities of AI have brought indispensable benefits to spine surgery, but their integration also introduces serious risks. AI-based systems often require access to sensitive patient data, raising concerns about privacy and cybersecurity.19 Inadequate safeguards can lead to data breaches or misuse by third parties. Furthermore, the use of AI tools in the operating room introduces questions of liability. For instance, if a robotic or navigation system malfunctions and causes harm, it remains unclear whether the responsibility lies with the surgeon, institution, or technology developer. As AI becomes more autonomous, it is critical to establish clear accountability standards. In clinical decision-making, overreliance on AI tools such as LLMs can compromise care. While LLMs like ChatGPT can support evidence-based reasoning, they may also generate responses based on inaccurate or non–peer-reviewed content, potentially misleading clinicians and patients.3,7,9 Additionally, many machine learning algorithms are trained on nonrepresentative datasets, which can perpetuate existing health disparities and reduce generalizability.15 As AI continues to evolve, surgeons must remain vigilant about its limitations, ensuring that these tools augment, not replace, human expertise and ethical judgment.

Future Directions

The future of AI in spine surgery will focus on improving algorithm accuracy, integrating real-time data, and enhancing clinical decision-making. Broader, more diverse datasets are needed to improve generalizability and reduce bias in predictive models.20 Regulatory frameworks must also evolve to ensure safety, transparency, and validation of AI tools before clinical deployment.21 Immersive technologies such as AR and VR are expected to become more integrated into surgical navigation and training, offering real-time guidance and personalized simulation.20,21 The convergence of AI with AR, robotics, and advanced imaging may lead to a more adaptive and precise surgical environment. Continued research, ethical oversight, and interdisciplinary collaboration will be key to safely advancing these innovations.21

Conclusion

AI is rapidly transforming the field of spine surgery, offering novel tools that enhance precision, efficiency, and personalization across the continuum of care. From immersive visualization and LLMs to machine learning and robotic assistance, these innovations are redefining surgical planning, intraoperative execution, and postoperative outcomes. Yet, the integration of AI also brings forth challenges, including data privacy concerns, accountability in clinical errors, and the risk of overreliance on potentially flawed algorithms.

As AI technologies continue to evolve, their success will depend on responsible implementation, rigorous validation, and thoughtful regulation. Spine surgeons must remain both innovative and vigilant in order to both embrace the benefits of AI while safeguarding patient care through ethical oversight and human expertise. The future lies in a balanced partnership between technology and the clinician.

References

1. Shi L, Wang H, Shea GK. The application of artificial intelligence in spine surgery: a scoping review. J Am Acad Orthop Surg Glob Res Rev. 2025;9(4):e24.00405.

2. Kothe R, Richter M. Relevanz der spinalen navigation in der rekonstruktiven halswirbelsäulenchirurgie [Relevance of spinal navigation in reconstructive surgery of the cervical spine]. Orthopade. 2018;47(6):518-525.

3. Rao SJ, Isath A, Krishnan P, et al. ChatGPT: a conceptual review of applications and utility in the field of medicine. J Med Syst. 2024;48(1):59.

4. Park DY, Park SM, Hashmi S, Lee YP, Bhatia N, Oh M. Enhancing endoscopic spine surgery with intraoperative augmented reality: a case report. Int J Surg Case Rep. 2025;131:111342.

5. Shah R, Schwab JH. Large language models in spine surgery: a promising technology. HSS J. Published online May 29, 2025. https://doi. org/10.1177/15563316251340696

6. Almekkawi AK, Caruso JP, Anand S, et al. Comparative analysis of large language models and spine surgeons in surgical dsecision-making and radiological assessment for spine pathologies. World Neurosurg. 2025;194:123531.

7. Lang SP, Yoseph ET, Gonzalez-Suarez AD, et al. Analyzing large language models' responses to common lumbar spine fusion surgery questions: a comparison between ChatGPT and Bard. Neurospine. 2024;21(2):633-641.

8. Sosa BR, Cung M, Suhardi VJ, et al. Capacity for large language model chatbots to aid in orthopedic management, research, and patient queries. J Orthop Res. 2024;42(6):1276-1282.

9. Stroop A, Stroop T, Zawy Alsofy S, et al. Large language models: are artificial intelligence-based chatbots a reliable source of patient information for spinal surgery? Eur Spine J. 2024;33(11):4135-4143.

10. Winkler D, Kropla F, Busse M, et al. Mixed reality for spine surgery: a step into the future with a human cadaveric accuracy study. Neurosurg Focus. 2024;56(1):E10.

11. Móga K, Hölgyesi Á, Zrubka Z, Péntek M, Haidegger T. Augmented or mixed reality enhanced head-mounted display navigation for in vivo spine surgery: a systematic review of clinical outcomes. J Clin Med. 2023;12(11):3788.

12. Bui T, Ruiz-Cardozo MA, Dave HS, et al. Virtual, augmented, and mixed reality applications for surgical rehearsal, operative execution, and patient education in spine surgery: a scoping review. Medicina (Kaunas). 2024;60(2):332.

13. Adida S, Legarreta AD, Hudson JS, et al. Machine learning in spine surgery: a narrative review. Neurosurgery. 2024;94(1):53-64.

14. Berg B, Gorosito MA, Fjeld O, et al. Machine learning models for predicting disability and pain following lumbar disc herniation surgery. JAMA Netw Open. 2024;7(2):e2355024.

15. Stephens ME, O'Neal CM, Westrup AM, et al. Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review. Neurosurg Rev. 2022;45(2):965-978.

16. Benzakour A, Altsitzioglou P, Lemée JM, Ahmad A, Mavrogenis AF, Benzakour T. Artificial intelligence in spine surgery. Int Orthop. 2023;47(2):457-465.

17. D'Antoni F, Russo F, Ambrosio L, et al. Artificial intelligence and computer vision in low back pain: a systematic review. Int J Environ Res Public Health. 2021;18(20):10909.

18. Comstock CP, Wait E. Novel machine vision image guidance system significantly reduces procedural time and radiation exposure compared with 2-dimensional fluoroscopy-based guidance in pediatric deformity surgery. J Pediatr Orthop. 2023;43(5):e331-e336.

19. Mudgal SK, Agarwal R, Chaturvedi J, Gaur R, Ranjan N. Real-world application, challenges and implication of artificial intelligence in healthcare: an essay. Pan Afr Med J. 2022;43:3.

20. Ghaednia H, Fourman MS, Lans A, et al. Augmented and virtual reality in spine surgery, current applications and future potentials. Spine J. 2021;21(10):1617-1625.

21. Hornung AL, Hornung CM, Mallow GM, et al. Artificial intelligence and spine imaging: limitations, regulatory issues and future direction. Eur Spine J. 2022;31(8):2007-2021.

Contributors:

Kristine Chong, BS

Brittany Morris, BS

John Carroll, BS

Kern Singh, MD

From the Department of Orthopaedic Surgery at Rush University Medical Center in Chicago, Illinois.