Automated radiology report generation: A comprehensive review

Abstract

As the workload of radiologists continues to increase, writing radiology reports remains a time-consuming and error-prone task. In recent years, Automated Radiology Report Generation (ARRG) has emerged as a research hotspot aimed at addressing this challenge. This review analyses the main ARRG research streams, including template-based, retrieval-based, encoder-decoder, foundation-model-based, and hybrid approaches. We trace the evolution of ARRG from early template and retrieval paradigms to encoder-decoder and more recent foundation-model-based approaches, while also discussing the growing roles of multimodal, knowledge integration and reinforcement learning strategies, and we compare their respective strengths and limitations. We further summarise the commonly used public, restricted, and private datasets in ARRG research, while distinguishing between datasets that can directly support report generation and auxiliary resources mainly used for pretraining, grounding, or evaluation. In addition, we examine the clinical implications of radiology report format, with particular attention to the trade-offs between free-text and structured reporting and their consequences for model design. We also review mainstream evaluation methods for ARRG, including quantitative metrics (e.g., NLG and CE metrics) and qualitative assessment, and discuss why factual correctness, report organization, and clinical usefulness are not fully captured by surface-level language similarity alone. Finally, we discuss ethical and governance issues that are especially salient for ARRG, such as privacy, bias, hallucination, omission of key abnormalities, negation errors, and responsibility allocation in clinical workflows. We hope that this review will serve as a useful reference for future ARRG research and for the safe translation of these systems into clinical practice.