Large language models (LLMs) and large vision models (LVMs) have driven significant advancements in natural language processing (NLP) and computer vision (CV), establishing a foundation for multimodal large language models (MLLMs) to integrate diverse data types in real-world applications. This survey explores the evolution of MLLMs in radiology, focusing on radiology report generation (RRG) and radiology visual question answering (RVQA), where MLLMs leverage the combined capabilities of LLMs and LVMs to improve clinical efficiency. We begin by tracing the history of radiology and the development of MLLMs, followed by an overview of MLLM applications in RRG and RVQA, detailing core datasets, evaluation metrics, and leading MLLMs that demonstrate their potential in generating radiology reports and answering image-based questions. We then discuss the challenges MLLMs face in radiology, including dataset scarcity, data privacy and security, and issues within MLLMs such as bias, toxicity, hallucinations, catastrophic forgetting, and limitations in traditional evaluation metrics. Finally, this paper proposes future research directions to address these challenges, aiming to help AI researchers and radiologists overcome these obstacles and advance the study of MLLMs in radiology.

Radiology report generation (RRG) aims to automatically produce diagnostic reports from medical images, with the potential to enhance clinical workflows and reduce radiologists' workload. While recent approaches leveraging multimodal large language models (MLLMs) and retrieval-augmented generation (RAG) have achieved strong results, they continue to face challenges such as factual inconsistency, hallucination, and cross-modal misalignment. We propose a multimodal multi-agent framework for RRG that aligns with the stepwise clinical reasoning workflow, where task-specific agents handle retrieval, draft generation, visual analysis, refinement, and synthesis. Experimental results demonstrate that our approach outperforms a strong baseline in both automatic metrics and LLM-based evaluations, producing more accurate, structured, and interpretable reports. This work highlights the potential of clinically aligned multi-agent frameworks to support explainable and trustworthy clinical AI applications.

Radiology visual question answering (RVQA) provides precise answers to questions about chest X-ray images, alleviating radiologists' workload. While recent methods based on multimodal large language models (MLLMs) and retrieval-augmented generation (RAG) have shown promising progress in RVQA, they still face challenges in factual accuracy, hallucinations, and cross-modal misalignment. We introduce a multi-agent system (MAS) designed to support complex reasoning in RVQA, with specialized agents for context understanding, multimodal reasoning, and answer validation. We evaluate our system on a challenging RVQA set curated via model disagreement filtering, comprising consistently hard cases across multiple MLLMs. Extensive experiments demonstrate the superiority and effectiveness of our system over strong MLLM baselines, with a case study illustrating its reliability and interpretability. This work highlights the potential of multi-agent approaches to support explainable and trustworthy clinical AI applications that require complex reasoning.