The growing demand for radiological services, amplified by a shortage of qualified radiologists, has resulted in significant challenges in managing the increasing workload while ensuring the accuracy and timeliness of radiological reports. To address these issues, recent advancements in artificial intelligence (AI), particularly in transformer models, vision-language models (VLMs), and Large Language Models (LLMs), have emerged as promising solutions for radiology report generation (RRG). These systems aim to make diagnosis faster, reduce the workload for radiologists by handling routine tasks, and help generate high-quality, consistent reports that support better clinical decision-making. This comprehensive study covers RRG developments from 2021 to 2025, focusing on emerging transformer-based and VLMs, highlighting the key methods, architectures, and techniques employed. We examine the datasets currently available for RRG applications and the evaluation metrics commonly used to assess model performance. In addition, the study analyzes the performance of the leading models in the field, identifying the top performers and offering insights into their strengths and limitations. Finally, this study proposes new directions for future research, emphasizing potential improvements to existing systems and exploring new avenues for advancing the capabilities of AI in radiology report generation.

Knowledge Graphs (KGs) are becoming an essential tool in the organization, linkage, and analysis of complex data across various domains. By structuring information as entities and their interrelationships, KGs enhance data integration and provide advanced capabilities such as reasoning, question-answering, and real-time decision-making. This paper explores methodologies for building and maintaining static and dynamic knowledge graphs, focusing on their applications in fields like virtual assistants, recommendation systems, autonomous systems, and AI-driven decision-making. The study emphasizes the growing significance of automation tools such as Named Entity Recognition (NER) and relationship extraction and addresses challenges in balancing automation with accuracy in KG construction. Furthermore, the transformation of static knowledge graphs into dynamic, continuously updating systems is discussed, showcasing their potential to revolutionize real-world applications by integrating ever-evolving datasets. In this context, dynamic KGs excel in domains that require real-time updates, making them invaluable for climate monitoring, autonomous vehicles, and production fault detection systems. This study provides insights into the methodologies and tools that can ensure high-quality KG creation, the challenges of real-time data integration, and how KGs contribute to advancing AI capabilities in data-rich environments.