Recent Progress in Deep Learning for Chest X-Ray Report Generation

Chest X-ray radiology report generation is a challenging task that involves techniques from medical natural language processing and computer vision. This paper provides a comprehensive overview of recent progress. The annotation protocols, structure, linguistic characteristics, and size of the main public datasets are presented and compared. Understanding their properties is necessary for benchmarking and generalization. Both clinically oriented and natural language generation metrics are included in the model evaluation strategies to assess their performance. Their respective strengths and limitations are discussed in the context of radiology applications. Recent deep learning approaches for report generation and their different architectures are also reviewed. Common trends such as instruction tuning and the integration of clinical knowledge are also considered. Recent works show that current models still have limited factual accuracy, with a score of 72% reported with expert evaluations, and poor performance on rare pathologies and lateral views. The most important challenges are the limited dataset diversity, weak cross-institution generalization, and the lack of clinically validated benchmarks for evaluating factual reliability. Finally, we discuss open challenges related to data quality, clinical factuality, and interpretability. This review aims to support researchers by synthesizing the current literature and identifying key directions for developing more clinically reliable report generation systems.