Imagine a world where dense, free-text radiology reports are instantly transformed into clear, structured data, easily understandable by both doctors and computers. This isn't science fiction, it's the promise of a new research breakthrough. Traditionally, converting these reports to a fully structured format has been a tedious, manual process. Researchers have now developed an open-source AI model that automates this conversion with remarkable accuracy. This innovative AI doesn't just reformat the text; it understands the content, extracting key features like nodule size, location, and Lung-RADS category without errors or hallucinations. The model achieves this by using a 'dynamic-template-constrained' approach, which ensures the AI follows a predefined structure, guaranteeing data consistency and eliminating the risk of fabricating information. Tested on a diverse, cross-institutional dataset, this AI outperforms even proprietary models like GPT-4, demonstrating its potential for real-world application. But what does this mean for the future of radiology? This AI can potentially revolutionize how radiologists work, providing real-time quality checks, simplifying follow-up reporting, and enabling large-scale data analysis across institutions. Imagine easily searching thousands of reports for specific nodule characteristics, instantly retrieving relevant images and statistics. This AI-powered tool unlocks this possibility, paving the way for more efficient research, improved patient care, and even the development of more sophisticated diagnostic AI tools.