Abstract
Background/Objectives: Lumbar magnetic resonance imaging (MRI) is widely overused in low back pain. We assessed the appropriateness of lumbar MRI requests against three international guidelines and two independent large language model (LLM)-based artificial intelligence (AI) assessments, related appropriateness to diagnostic yield, and derived a simple decision rule to identify imaging likely to change management. Methods: In this single-centre retrospective study, 147 consecutive adults undergoing lumbar MRI for low back pain were analysed. Each request was classified using pre-imaging clinical variables according to operationalised ACR, NICE and ACP criteria and by two blinded LLM assessments (Claude, Anthropic; ChatGPT, OpenAI). The reference outcome was a management-changing MRI (subsequent surgery or interventional procedure); a four-item decision rule was derived by exhaustive search over clinical criteria optimising the Youden index. Results: Depending on the instrument, 57.8–72.1% of requests were inappropriate (ACR 66.0%; NICE 72.1%; ACP 66.0%; AI 57.8–64.6%). Agreement between instruments was substantial to near-perfect (κ 0.69–0.94; inter-AI κ 0.77). Red flags or neurological warning features were present in only 5.4%. MRI changed management in 33.3% overall—58.1–80.5% of appropriate versus 15.1–16.8% of inappropriate requests. A rule of four criteria—red flag or neurological warning feature, neurogenic claudication, purely radicular pain, or pre-imaging consideration of surgery/intervention—predicted management-changing MRI with 84% sensitivity and 81% specificity (Youden 0.64, versus 0.50–0.59 for the guidelines) and would have reduced imaging volume by 59.2%. Conclusions: Roughly two-thirds of lumbar MRI requests were guideline-inappropriate and rarely altered management. In this exploratory derivation cohort, a simple four-item rule showed promising predictive performance, but its apparent numerical advantage over existing guideline logic was not statistically confirmed and requires independent prospective validation; two blinded LLM assessments showed substantial agreement with guideline judgements, supporting further evaluation of LLM-based pre-order screening in independently validated settings.