Automated radiology report generation aims to create clear and clinically correct diagnostic reports from medical images. Existing retrieval enhancement methods primarily focus on reusing textual knowledge, neglecting the crucial role of local visual pattern memory in clinical diagnosis. Furthermore, cross-modal retrieval lacking explicit clinical semantic constraints can easily introduce irrelevant pathological information, thereby reducing the clinical effectiveness of the generated reports. To address these challenges, we propose ClinAlign—a memory-based retrieval framework aligned with clinical workflow, drawing inspiration from clinical diagnostic workflows. Visually, we construct a disease-aware visual memory bank and enhance local patch representations through proposed Memory‑based Patch Pattern Augmentation (MPPA), thereby improving the perception and discrimination of pathological regions. On the textual side, we construct a disease-aware textual memory bank and introduce Classification-Guided Prompt Augmentation (CGPA), where disease state predictions are converted into structured diagnostic prompts to provide explicit semantic guidance for textual memory retrieval.Extensive experiments on two medical report generation benchmarks, MIMIC-CXR and IU X-Ray, demonstrate the effectiveness and practical value of our proposed method.