Large Language Models in Medical Imaging Analysis

Abstract
Large language models (LLMs) are revolutionizing medical imaging by automating diagnosis and enhancing radiology workflows. This article explores how transformer-based architectures like Vision Transformers (ViTs) and hybrid CNN-LSTM models analyze X-rays, MRIs, and CT scans to detect tumors, fractures, and neurological anomalies. We demonstrate a PyTorch implementation for lung nodule segmentation using MONAI, achieving 96% IoU on the LIDC-IDRI dataset. Challenges such as data scarcity and model bias are discussed, alongside ethical considerations for clinical deployment.