Large language models for lumbar spondylolisthesis detection: a multi-center pilot comparative radiographic accuracy study

Prompt Configurations for Multimodal Large Language Models in Diagnosing and Staging Osteonecrosis of the Femoral Head: Multimodel Retrospective Observational Diagnostic Study

The findings support the potential role of MLLMs as assistive tools in human-AI orthopedic imaging workflows, but external validation, careful input standardization, and prospective clinical evaluation are needed before clinical deployment.

Multimodal Large Language Models vs. Medical Doctors in Degenerative Lumbar Spine Surgery: A Retrospective Decision Concordance Study of 147 Patients

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.

Diagnostic performance of GPT-5.2 ınstant for pediatric elbow fracture detection on radiographs: a prospective single-center diagnostic accuracy study

Multimodal large language models can interpret medical images, but their performance for pediatric elbow radiographs remains uncertain. We evaluated the diagnostic performance of GPT-5.2 Instant as accessed through the ChatGPT web interface during the defined study period. In this prospective, single-cente...

Endoscopic decompression alone for lumbar spinal stenosis with degenerative spondylolisthesis: a systematic review and meta-analysis of paired change scores.

Large Language Models for Ankle Fracture Classification and Management Prediction from Routine Clinical Documentation: A Single-Center Exploratory Study.

It is suggested that LLMs can extract structured information from routine clinical documentation, performing well for standardized classification but less reliably for procedure-level prediction.

Multicenter evaluation of four large language models for automated spine imaging diagnosis

Accurate interpretation of spine imaging is essential for clinical decision-making, yet the diagnostic potential of large language models (LLMs) for radiological report analysis remains inadequately evaluated in terms of sample size, multi-model comparison, reproducibility, and cross-institutional generalisability. Her...