Multimodal Large Language Models vs. Medical Doctors in Degenerative Lumbar Spine Surgery: A Retrospective Decision Concordance Study of 147 Patients
TL;DR
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.
Abstract
Objective: To evaluate decision concordance between commercially available multimodal large language models (LLMs), resident doctors, and senior-surgeon ground truth for surgical indication and spinal level in degenerative lumbar spine disease. Methods: We retrospectively analyzed 147 consecutive patients. Each case included clinical documentation and MRI presented as two composite PNG images. Two resident doctors and three multimodal LLMs (GPT 5.5, Claude Sonnet 4.6, Gemini 3.1 Pro) independently assessed operative versus conservative management and, if operative, the surgical level. Analyses used Cochran's Q, McNemar tests with Holm correction, and Bayesian methods. Results: LLMs achieved higher therapy-decision accuracy (66.0%-68.0%; 97-100/147) than residents (54.4%; 80/147) but over-recommended surgery. Conditional level accuracy when surgery was correctly indicated was 71.4% (20/28) for residents versus 33.3%-41.1% for LLMs. Conclusion: Off-the-shelf multimodal LLMs approximate human performance for binary surgical indication but remain inferior for precise level localization. These results establish a practice-relevant baseline of spatial reasoning limitations for tools already used by patients and junior doctors.