Self-Reflective Chest X-Ray Report Generation with Clinical-Aware Detection and Multilevel Readability.

ABSTRACT: Clinical documentation demands necessitate automated solutions balancing clinical precision with patient comprehension. This study aims to develop and validate a unified framework that maintains diagnostic accuracy while dynamically adapting medical report complexity to diverse literacy levels, and to establish comprehensive evaluation methodologies for patient-centered medical documentation. We developed a unified framework integrating three innovations: a hybrid detection method combining CheXFusion and Eigen-CAM for clinical finding detection and anatomical localization; an advanced LLaVA-based pipeline synthesizing clinical predictions with anatomical data for contextually rich medical reports; and a self-reflective large language model system dynamically adapting report complexity acr

SUBMITTER: Han J 

PROVIDER: S-EPMC12743066 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature