Large Language Models in Spine Surgery: A Clinical Decision- Making Framework for the Next Decade with Emphasis on Degenerative Spine Care and LMIC Applications

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.