Medical AI needs dual optimization: factual correctness (verifiable through evidence) and patient communication quality (context-dependent). G-CARL shows that structured checklists paired with retrieval-based verification can train models for both simultaneously better than standard approaches.
This paper introduces a new task where AI systems explain medical reports to patients in accurate, accessible language. The key innovation is G-CARL, a training method that uses retrieval-based fact-checking and customized checklists to ensure explanations are both medically accurate and responsive to what patients actually want to know, without limiting creative variation in responses.