Large language models in spine care and research

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.

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.

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.

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.