Large Language Models in Spine Surgery: A Clinical Decision- Making Framework for the Next Decade with Emphasis on Degenerative Spine Care and LMIC Applications
Jul 2026· Nepal Journal of Neuroscience· Vol 23, pp. 4-9· 0 citations· 28 references
TL;DR
A narrative review examines the evolving role of artificial intelligence in spine surgery, with particular emphasis on large language models in degenerative conditions of the cervical and lumbar spine, and proposes a structured six-level clinical decision-making framework spanning initial patient contact to postoperative care.
Abstract
Degenerative spine disorders are a leading cause of disability worldwide and impose a growing burden on health systems, particularly in low- and middle-income countries. Large language models are emerging as powerful tools capable of supporting clinical decision-making, synthesising complex evidence, generating patient-specific explanations, and improving clinical documentation. Their ability to process free-text clinical narratives and integrate multiple sources of information makes them particularly suited to degenerative spine care, where decision-making requires the integration of symptoms, neurological findings, imaging, and patient preferences.
This narrative review examines the evolving role of artificial intelligence in spine surgery, with particular emphasis on large language models in degenerative conditions of the cervical and lumbar spine. We describe current and emerging applications across clinical triage, radiological interpretation, guideline synthesis, patient communication, and workflow optimisation. Building on these insights, we propose a structured six-level clinical decision-making framework spanning initial patient contact to postoperative care.
We also discuss key ethical, medico-legal, and governance considerations relevant to the safe implementation of these technologies, particularly in resource-constrained environments. Large language models are unlikely to replace clinical judgement; however, when integrated within structured workflows and appropriate safety systems, they have the potential to enhance the quality, efficiency, and equity of degenerative spine care globally over the coming decade.
BACKGROUND
Large language models (LLMs) have emerged as powerful transformer-based systems capable of capturing long-range dependencies and complex semantic relationships in clinical language. In this review paper, we first examine the technical foundations of medical LLMs, including transformer architecture, attention mechanisms, training paradigms, and retrieval-augmented generation.
RESULTS
We then survey documented applications in spine surgery and spinal care, highlighting moderate guideline concordance (46-67%) for diagnostic support, automated generation of operative notes and discharge summaries for administrative workflows, LLM-assisted literature review and manuscript drafting for research support (with ~68% novelty accuracy), and translation of complex surgical concepts into patient-friendly materials at a seventh-grade reading level. We next explore emerging multimodal models that integrate text, imaging, laboratory, and genomic data via cross-modal attention, demonstrating superior performance in holistic diagnostic and prognostic tasks.
DISCUSSION
Finally, we discuss key implementation challenges, including model accuracy and hallucinations; computational, privacy, and regulatory constraints under HIPAA/GDPR; and bias mitigation, to outline strategies for safe, effective, and equitable deployment.
CONCLUSION
By mapping technical capabilities to clinical and research use cases, this review highlights the promise of LLMs in enhancing decision support, workflow efficiency, research productivity, and patient communication in spine care, while emphasizing the need for interdisciplinary collaboration, robust evaluation metrics, and governance frameworks that prioritize patient safety and equity.
Fabio Galbusera, Andrea Cina· European spine journal· 0 citations
Study Design Scoping review. Objectives To map spine literature on large language models, characterize reported use cases, and identify evidence gaps limiting implementation. Methods A scoping review was conducted according to Joanna Briggs Institute methodology and PRISMA-ScR guidance. PubMed, Embase, Scopus, Web of Science, and Cochrane were searched for English-language, peer-reviewed studies published from January 2023 through May 2026 that evaluated large language models in spinal disease, spine surgery, or spine-related care. Eligible studies were synthesized across clinical decision support, triage, patient communication, automation, surgical education, and implementation barriers. Results Fifteen studies met inclusion criteria. Most evidence involved early evaluation of commercially available or general-purpose models rather than prospectively validated spine-specific systems. Reported applications included patient education, report simplification, coding support, emergency consultation simulation, spinal cord stimulation referral screening, conservative triage, and surgical education. Performance was strongest for structured text-based tasks, patient communication, documentation support, and simplified decision pathways. Performance was weaker for image interpretation, quantitative radiographic assessment, individualized operative planning, and granular procedure selection. Recurrent limitations included hallucinated or unsupported outputs, unreliable citation generation, limited multimodal capability, privacy and data-governance concerns, bias, unclear medicolegal accountability, and minimal validation. Conclusions Large language models are an adjunct in spine surgery, with the near-term role in clinician-supervised, text-centered workflows including patient communication, education, documentation, coding, guideline retrieval, and preliminary triage. Current evidence does not support autonomous diagnostic, radiographic, or operative decision-making. Future studies should prioritize spine-specific retrieval-augmented systems, validated multimodal workflows, privacy-preserving deployment, fairness assessment, and prospective evaluation using clinically meaningful outcomes.
Samer G. Salman, R. Phadke, Anne E Tatooles et al.· Global Spine Journal· 0 citations
Off-the-shelf multimodal LLMs approximate human performance for binary surgical indication but remain inferior for precise level localization, establish a practice-relevant baseline of spatial reasoning limitations for tools already used by patients and junior doctors.
M. Hamdan, A. Harati, A. Al-bakheet et al.· medRxiv· 0 citations
Background Artificial intelligence (AI) is increasingly used to enhance diagnostic accuracy, automate image interpretation, and support clinical decision-making. In the field of spine care, applications include MRI and CT-based detection of lumbar disc degeneration, spinal stenosis, vertebral fractures, and axial spondyloarthritis, as well as emerging symptom-based and multimodal diagnostic tools. However, evidence remains dispersed across modalities and conditions, and the quality and clinical readiness of AI systems vary. This scoping review maps current AI applications for diagnosing spinal disorders and identifies gaps for future research and clinical translation. Methods This review followed Joanna Briggs Institute (JBI) and PRISMA-ScR guidelines. Ovid MEDLINE, AMED, Embase, Cochrane CENTRAL, Web of Science, and Scopus were searched from January 2019 to December 2024. Eligible studies were mapped according to AI methodology, diagnostic target, data source, and validation approach, and were required to involve human participants, include sufficient methodological detail, and published in English peer-reviewed journals. No geographic restrictions were applied. Data was extracted on study design, AI methodology, diagnostic target, validation approach, and usability. Methodological quality was assessed using a 19-point scoring system covering study design, reporting clarity, data validation, and feature selection. Results Forty-six studies met the inclusion criteria, conducted primarily in Asia and Europe, with two studies from North America and one from South America. Most investigations were retrospective, imaging-based deep learning models applied to MRI or CT for detecting disc herniation, lumbar spinal stenosis, modic changes, vertebral fractures, and sacroiliitis. Several studies used prospective designs or external validation. Diagnostic performance was generally high across imaging models, with many studies describing accuracy that approached or matched clinician benchmarks, particularly in sacroiliitis classification, disc disease detection, and stenosis grading. Methodological scores ranged from 7.5 to 17.5 out of 19, with recurrent weaknesses in handling missing data, feature selection, and data element validation. Conclusion This review maps a growing body of literature on AI applications for diagnosing spinal disorders, with studies most frequently reporting favorable performance for MRI- and CT-based detection of degenerative and inflammatory conditions. Evidence remains preliminary and heterogeneous.
Victoria A. Bensel, Anne Habeck, Marcda Hilaire Brunot et al.· PLoS ONE· 0 citations
Errors in differential diagnosis often arise while clinicians are generating and comparing candidate explanations. This review examines the use of large language models (LLMs) for this part of diagnostic reasoning. Internal medicine and pediatrics are the main focus; evidence from radiology, surgical subspecialties, infectious disease, and mental health is used to examine how findings change across specialties. Reported performance depends on the clinical setting, the quality of the input, the prompt, model adaptation, and the evaluation design. Some studies place LLMs near trainees and find that they produce wider, better-organized differentials. Experienced clinicians, however, remain more reliable overall. Domain adaptation, external knowledge, and interactive workflows have improved performance in specific evaluations, but hallucinations and automation bias remain, alongside unresolved questions of governance. Current evidence therefore supports clinician-supervised use of artificial intelligence (AI) systems rather than autonomous diagnosis, pending prospective and specialty-specific evaluation.
Yun-Jia Wu, Qi Yan, Dingcheng Tian· AI Medicine· 0 citations
Musculoskeletal diseases are among the leading causes of disability and drive the greatest global need for rehabilitation. Because recovery, remodelling and degeneration of bones, joints and related tissues unfold over months to years, care requires longitudinal management rather than isolated decisions. Clinicians must repeatedly integrate evolving patient evidence, medical knowledge and stage-specific functional goals, yet evidence is often fragmented across visits, departments and hospital systems, disrupting continuous, individualised management. Here we report OrthoPilot, a clinical artificial intelligence (AI) system powered by a large language model (LLM) that integrates hospital data streams with authoritative external knowledge for continuous musculoskeletal care. It autonomously retrieves real-time imaging, laboratory, pathology and order data and translates evolving patient states into evidence-based decisions from admission diagnosis through rehabilitation planning. We established a specialist-validated benchmark from real-world electronic health records (EHRs) spanning 1,000 disease codes. In a full-pathway reader study against 81 orthopaedic physicians, OrthoPilot outperformed experts with 25 years of experience in diagnostic reasoning, clinical decision-making and management planning. This advantage generalised across 60 external clinical centres, where OrthoPilot surpassed all evaluated intelligent systems. In a prospective physician decision-making study of 1,870 complex cases, OrthoPilot improved full-chain management success by 10.6%. In a randomised deployment involving 8,240 inpatients, integration into routine care increased cumulative cases per bed by 9.7% and improved patient-reported access to health information. These results move clinical AI from predicting isolated events toward executing longitudinal management across complete musculoskeletal care pathways.
Wenjie Li, Yu-Jie Zhang, Fanrui Zhang et al.· 0 citations