A review of longitudinal radiology report generation: Dataset composition, methods, and performance evaluation.

Abstract

Chest X-ray imaging is a widely used diagnostic tool in modern medicine, and its high utilization creates substantial workloads for radiologists. To alleviate this burden, vision-language models are increasingly applied to automate Chest X-ray radiology report generation (CXR-RRG), aiming for clinically accurate descriptions while reducing manual effort. Conventional approaches, however, typically rely on single image, failing to capture the longitudinal context necessary for producing clinically faithful comparison statements. Recently, growing attention has been directed toward incorporating longitudinal data into CXR-RRG, enabling models to leverage historical studies in ways that mirror radiologists' diagnostic workflows. Nevertheless, existing surveys primarily address single image CXR-RRG and offer limited guidance for longitudinal settings, leaving researchers without a systematic framework for model design. To address this gap, this survey provides the first comprehensive review of longitudinal radiology report generation (LRRG). Specifically, we examine dataset construction strategies, report generation architectures alongside longitudinally tailored designs, and evaluation protocols encompassing both longitudinal-specific measures and widely used benchmarks. We further summarize LRRG methods' performance, alongside analyses of different ablation studies, which collectively highlight the critical role of longitudinal information and architectural design choices in improving model performance. Finally, we summarize six major limitations of current research and outline promising directions for future development, aiming to lay a foundation for advancing this emerging field.