Large language models for structured reporting in radiology: past, present, and future

Structured reporting (SR) has long been a goal in radiology to standardize and improve the quality of radiology reports. Despite evidence that SR reduces errors, enhances comprehensiveness, and increases adherence to guidelines, its widespread adoption has been limited. Recently, large language models (LLMs) have emerged as a promising solution to automate and facilitate SR. Therefore, this narrative review aims to provide an overview of LLMs for SR in radiology and beyond. We found that the current literature on LLMs for SR is limited, comprising ten studies on the generative pre-trained transformer (GPT)-3.5 (n = 5) and/or GPT-4 (n = 8), while two studies additionally examined the performance of Perplexity and Bing Chat or IT5. All studies reported promising results and acknowledged the potential of LLMs for SR, with six out of ten studies demonstrating the feasibility of multilingual applications. Building upon these findings, we discuss limitations, regulatory challenges, and further applications of LLMs in radiology report processing, encompassing four main areas: documentation, translation and summarization, clinical evaluation, and data mining. In conclusion, this review underscores the transformative potential of LLMs to improve efficiency and accuracy in SR and radiology report processing. KEY POINTS: Question How can LLMs help make SR in radiology more ubiquitous? Findings Current literature leveraging LLMs for SR is sparse but shows promising results, including the feasibility of multilingual applications. Clinical relevance LLMs have the potential to transform radiology report processing and enable the widespread adoption of SR. However, their future role in clinical practice depends on overcoming current limitations and regulatory challenges, including opaque algorithms and training data.