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The paper introduces a method using corpus characterization and inverse constitutional fine-tuning to improve the stylistic alignment of AI-generated radiology reports with authentic radiologist writing. This approach achieves significant gains in text alignment metrics, demonstrating effectiveness for style-aware report generation.
RadAgent is a tool-using AI agent that generates chest CT reports through interpretable step-by-step reasoning, improving clinical accuracy by 36.4% relative and achieving 37% faithfulness—a capability absent in existing 3D vision-language models. The system provides fully inspectable reasoning traces allowing clinicians to validate and refine diagnostic outputs.