Fiabilidad diagnóstica de la inteligencia artificial multimodal vs. Juicio clínico en la clasificación de fracturas AO/OTA: un estudio exploratorio Diagnostic reliability of multimodal artificial intelligence vs. Clinical judgment in AO/OTA fracture classification: an exploratory study

Introduction

The AO/OTA classification is the global standard for fracture categorization, although it presents interobserver variability, especially among personnel in training. Multimodal large language models (LLMs) are emerging as a diagnostic alternative to traditional convolutional neural networks (CNNs). Materials and Methods: A cross-sectional comparative study. Fifty radiographs of long bones, pelvis, and articular surfaces were selected. The sample was evaluated by the Gemini 3 Flash model and by residents of the UDAOT-ULA, using a consensus of specialists as the Gold Standard. SPSS v.29 software was used to calculate accuracy, sensitivity, specificity, and concordance using Cohen's Kappa index (p < 0.05).