The field of radiology is witnessing significant advancements with the integration of artificial intelligence (AI), particularly through large language models (LLMs). Recent developments have focused on improving the accuracy and reliability of radiology question answering (QA) systems, with a shift towards more complex and nuanced approaches. The use of agentic frameworks, multi-agent systems, and multimodal large language models has shown great promise in enhancing factual accuracy, reducing hallucinations, and improving diagnostic accuracy. These innovative approaches have the potential to revolutionize clinical decision-making and improve patient outcomes. Noteworthy papers include: Agentic large language models improve retrieval-based radiology question answering, which proposes an agentic RAG framework that enables LLMs to autonomously decompose radiology questions and iteratively retrieve targeted clinical evidence. CX-Mind: A Pioneering Multimodal Large Language Model for Interleaved Reasoning in Chest X-ray via Curriculum-Guided Reinforcement Learning, which introduces a novel generative model that achieves interleaved think-answer reasoning for CXR tasks, driven by curriculum-based reinforcement learning and verifiable process rewards.