Radiology reports encode critical clinical observations from medical imaging in an unstructured textual form that is central to modern clinical diagnosis and decision support. In this context, natural language processing (NLP) has emerged as a key clinical NLP technology for automatically extracting, classifying, and interpreting information from radiology reports. This study presents a structured review of more than sixty recent contributions on NLP for radiology, covering approaches that range from traditional rule-based pipelines to contemporary deep learning and transformer-based models. We examine how deep learning architectures, including BERT, GPT-4, multimodal transformers, and vision–language alignment networks, are applied to core tasks such as disease classification, tumor response assessment, cancer phenotype extraction, radiology report generation, cohort identification, quality assurance, and longitudinal patient follow-up. Particular attention is given to knowledge graph integration, multimodal cross-attention, and zero-shot learning strategies that adapt large language models to radiology-specific workflows. We also analyze key barriers to clinical adoption, including limited annotated data, domain generalization gaps across institutions, ethical and fairness concerns, and the need for transparent model explainability. Based on this synthesis, the review outlines future research directions for building interpretable, multimodal, and clinically robust NLP solutions that integrate technological, clinical, and operational perspectives to advance radiology report analysis and medical imaging–driven care.
Chandrakala et al. (Wed,) studied this question.