RADAR: A Difference-Aware Retrieval with Organ-Level Alignment for Change Radiology Report Generation

Automated report generation is limited to static, single-image analysis, failing to address the critical clinical need for longitudinal comparison in monitoring disease progression and treatment efficacy. To bridge this gap, we introduce the new task of Change Radiology Report Generation (CRRG) which aims to automatically generate a comparative radiology report describing interval changes between a patient's current and prior radiological images. To address this challenge, we propose RADAR, a novel framework that integrates deep visual comparison with knowledge-rich text generation. For robust visual analysis, RADAR employs an "align first, then compare" strategy via an organ-level alignment module, to first mitigate non-pathological artifacts before precisely identifying key pathological changes. Our text generation framework integrates soft prompts for visual evidence with a structured four-step workflow for clinical reasoning. Factual accuracy is enhanced by our Knowledge-Infused Generation (KIG) component, which dynamically retrieves knowledge from a database of similar pathological cases. To anchor our newly proposed task of CRRG and facilitate robust evaluation, we introduce the first comprehensive benchmark, featuring a meticulously curated and processed dataset. Extensive experiments on our benchmark demonstrate that RADAR outperforms existing methods on most evaluation metrics. Our work successfully elevates automated radiological assessment from static analysis to dynamic monitoring, producing interpretable and clinically relevant change reports for longitudinal comparison.