Trends and Trajectories in the Rise of Large Language Models in Radiology: Scoping Review.

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Abstract

The use of large language models (LLMs) in radiology is expanding rapidly, offering new possibilities in report generation, decision support, and workflow optimization. However, a comprehensive evaluation of their applications, performance, and limitations across the radiology domain remains limited. This review aimed to map current applications of LLMs in radiology, evaluate their performance across key tasks, and identify prevailing limitations and directions for future research. A scoping review was conducted in accordance with the framework by Arksey and O'Malley framework and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Three databases-PubMed, ScopusCOPUS, and IEEE Xplore-were searched for peer-reviewed studies published between January 2022 and December 2024. Eligible studies included empirical evaluations of LLMs applied to radiological data or workflows. Commentaries, reviews, and technical model proposals without evaluation were excluded. Two reviewers independently screened studies and extracted data on study characteristics, LLM type, radiological use case, data modality, and evaluation metrics. A thematic synthesis was used to identify key domains of application. No formal risk-of-bias assessment was performed, but a narrative appraisal of dataset representativeness and study quality was included. A total of 67 studies were included. (n/N, %)GPT-4 was the most frequently used model (n=28, 42%), with text-based corpora as the primary type of data used (n=43, 64%). Identified use cases fell into three thematic domains: (1) decision support (n=39, 58%), (2) report generation and summarization (n=16, 24%), and (3) workflow optimization (n=12, 18%). While LLMs demonstrated strong performance in structured-text tasks (eg, report simplification with >94% accuracy), diagnostic performance varied widely (16%-86%) and was limited by dataset bias, lack of fine tuning, and minimal clinical validation. Most studies (n=53, 79.1%) had single-center, proof-of-concept designs with limited generalizability. LLMs show strong potential for augmenting radiological workflows, particularly for structured reporting, summarization, and educational tasks. However, their diagnostic performance remains inconsistent, and current implementations lack robust external validation. Future work should prioritize prospective, multicenter validation of domain-adapted and multimodal models to support safe clinical integration.

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