Current Landscape of Automatic Radiology Report Generation with Deep Learning: An Exploratory Systematic Review

Automatic radiology report generation (ARRG) has emerged as a promising application of deep learning (DL) with the potential to alleviate reporting workload and improve diagnostic consistency. However, despite rapid methodological advances, the field re-mains technically fragmented and not yet mature for routine clinical adoption. This systematic review maps the current ARRG research landscape by examining DL archi-tectures, multimodal integration strategies, and evaluation practices from 2015 to April 2025. A PRISMA-compliant search identified 89 eligible studies, revealing a marked predominance of chest radiography datasets (87.6%), largely driven by their public availability and the accelerated development of automated tools during the COVID-19 pandemic. Most models employed hybrid architectures (73%), particularly CNN–Transformer pairings, reflecting a shift toward systems capable of combining local feature extraction with global contextual reasoning. Although these approaches have achieved measurable gains in textual and semantic coherence, several challenges per-sist, including limited anatomical diversity, weak alignment with radiological reason-ing, and evaluation metrics that insufficiently reflect diagnostic adequacy or clinical impact. Overall, the findings indicate a rapidly evolving but clinically immature field, underscoring the need for validation frameworks that more closely reflect radiological practice and support future deployment in real-world settings.