Imagine a world where AI seamlessly integrates with medical imaging, streamlining workflows and empowering radiologists with unprecedented insights. This isn't science fiction—it's the promise of Large Language Models (LLMs) in radiology. Recent research has explored how these powerful AI tools can transform everything from report generation and summarization to complex image analysis and even research and education. LLMs are poised to act as intelligent assistants, capable of understanding nuanced language, extracting key information from reports, and even helping radiologists draft recommendations based on established guidelines. This goes beyond simply automating tasks; it's about augmenting human expertise, allowing radiologists to focus on the most critical aspects of patient care. But it’s not all smooth sailing. The research also highlights crucial challenges like ensuring data privacy, mitigating AI bias, and addressing the tendency of LLMs to generate incorrect information, often called “hallucinations.” To effectively implement LLMs in radiology, a strategic approach is essential. The research suggests an iterative process, starting with carefully crafted prompts to guide the LLM’s behavior. Then, enriching these prompts with external information, like medical guidelines and patient records, can significantly improve accuracy and reduce errors. Finally, fine-tuning the LLM on specialized radiology data can further enhance its performance on specific tasks. This collaborative effort between radiologists and computer scientists is crucial to unlocking the full potential of LLMs, ensuring these powerful tools are used responsibly and effectively to enhance patient care and advance the field of radiology.