How I Do It: Understanding and Leveraging Generative AI for Chest Radiology Reporting.

Abstract

Generative artificial intelligence (AI) has introduced new possibilities for assisting radiologists in diagnostic interpretation and workflow, particularly through automated report generation. Chest radiography has emerged as a leading use case for generative AI due to its high clinical volume, its diagnostic complexity, and the availability of large public datasets. This article describes a practical, experience-based approach to understanding and implementing generative AI for chest radiograph reporting. It outlines key concepts and the underlying architecture of generative models, followed by a clinical framework that positions AI as a draft assistant, highlighting both its utility and limitations. Through representative examples, the article demonstrates how generative AI can accelerate workflow, promote reporting consistency, and support trainee education, while also addressing known pitfalls such as hallucinations. It discusses implementation strategies, interface considerations, and safety checkpoints that support effective human-in-the-loop review. The article concludes with future directions that emphasize the importance of context-aware modeling, explainability, and multi-institutional evaluation. The aim is to equip radiologists with essential knowledge and insights to engage safely and effectively with generative AI tools in thoracic radiology practice, using chest radiography as a paradigm for broader integration across diagnostic imaging.