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Radiology report generation aims to automatically provide clinicallymeaningful descriptions of radiology images such as MRI and X-ray. Althoughgreat success has been achieved in natural scene image captioning tasks,radiology report generation remains challenging and requires prior medicalknowledge. In this paper, we propose PromptRRG, a method that utilizes promptlearning to activate a pretrained model and incorporate prior knowledge. Sinceprompt learning for radiology report generation has not been explored before,we begin with investigating prompt designs and categorise them based on varyinglevels of knowledge: common, domain-specific and disease-enriched prompts.Additionally, we propose an automatic prompt learning mechanism to alleviatethe burden of manual prompt engineering. This is the first work tosystematically examine the effectiveness of prompt learning for radiologyreport generation. Experimental results on the largest radiology reportgeneration benchmark, MIMIC-CXR, demonstrate that our proposed method achievesstate-of-the-art performance. Code will be available upon the acceptance.

Can Prompt Learning Benefit Radiology Report Generation?