This research addresses fundamental challenges in automated radiology report generation (RRG) systems while introducing improved evaluation metrics that better reflect clinical utility.

• Template Dependency Problem: Existing RRG systems can achieve high metric scores by simply mimicking report templates rather than providing accurate diagnoses
• Enhanced Evaluation Framework: Introduces robust metrics that more fairly assess RRG performance across different models
• Clinical Relevance: Improves the quality of automated medical documentation, potentially reducing radiologist workload and improving patient care
• Practical Applications: Demonstrates how better evaluation metrics lead to more clinically valuable AI-generated reports

Original Paper: X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation