Framework for the Design and Evaluation of Patient-Friendly AI-Generated Medical Reports

Abstract

The difficulty of understanding radiology reports is well recognized as a direct result of the ubiquitous use of medical terminology. Recent research within the field has demonstrated that artificial intelligence can be leveraged to generate patientfriendly reports. These reports can be produced either through fine-tuning domain-specific models or by prompting existing models with task-specific instructions. This study proposes a conceptual framework to guide patient-friendly report generation, introducing multiple considerations to evaluate reports across content, readability, tone, and empathy. Future work will focus on validation, model fine-tuning for medical contexts, and the refinement of evaluation metrics to ensure reliable and consistent performance.

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