Use of Large Language Models on Radiology Reports: A Scoping Review

Large language models (LLMs) are increasingly being explored for a wide range of applications in radiology, offering the potential to enhance clinical workflows, improve diagnostic accuracy, and support patient communication. In this scoping review the authors examine the current and emerging uses of LLMs on radiology text, focusing on areas such as report generation, structured data extraction, workflow optimization, and clinical decision support. A literature search was conducted on PubMed and Embase, and a total of 69 articles were included in the review. The capabilities and limitations of existing approaches were assessed, and key methodologic considerations were discussed, including transparency and bias, while identifying critical gaps in validation and generalizability. Overall, LLMs demonstrated strong performance in workflows such as report simplification and translation but produced mixed results in classification tasks. Certain methods such as fine-tuning and structured prompt generation improved LLM accuracy. In assessing the characteristics of the included studies, although most studies performed well in documenting the independence of their testing and training datasets and LLM prompting methods, fewer than half of studies explicitly attempted to manage the inherent stochasticity of LLMs. By synthesizing recent advancements and outlining future directions, the aim of this review was to guide clinicians, researchers, and health care stakeholders in responsibly harnessing the transformative potential of LLMs in radiologic care.