The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI

Abstract

The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs’ potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.

Evaluation of T2W FLAIR MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain

“…One of the challenges with the application of AI reconstruction algorithms is the presence of “hallucinations,” in which the model generates incorrect data that was not present in the training data [ 14 ]. We attempted to address this by specifically examining the reproducibility of pathology and normal variants on the AI-reconstructed images, along with assessing for the presence or absence additional pathologies on the AI-reconstructed images.…”