Radiology Report Generation via Self‐Critical Multisource Knowledge Reasoning With Large Language Models

Automated radiology report generation (RRG) aims to reduce radiologist workload while improving reporting consistency and clinical reliability. Despite its importance for diagnosis, manual reporting remains time‐consuming and error‐prone. Existing encoder–decoder models often struggle to align visual and textual representations, resulting in incomplete or inconsistent reports. Large language models (LLMs) provide strong linguistic fluency for automated RRG, but most existing approaches still follow a linear generation paradigm and lack explicit reasoning, self‐evaluation, and structured clinical grounding. To address these limitations, we propose SMK–RRG, a self‐critical multisource Knowledge framework for RRG. The proposed framework integrates structured clinical knowledge, pathology‐aware visual evidence, and iterative self‐refinement within a unified LLM‐driven architecture. It is implemented through self‐critical thought (SCT) prompting, which performs multistage candidate report generation, LLM‐based self‐critique, and CheXbert‐guided clinical selection among candidate reports. By enabling critique‐guided prompt evolution across refinement stages, SCT moves beyond conventional chain‐based prompting and supports nonlinear, clinically grounded reasoning. Experiments on public chest X‐ray benchmarks demonstrate that SMK–RRG produces more accurate, interpretable, and clinically consistent reports, highlighting the effectiveness of self‐critique and structured information integration for reliable clinical reports.