Udomlapsakul K.Pengpun P.Saengja T.Veerakanjana K.Tiankanon K.Khlaisamniang P.Supholkhan P.Chinkamol A.Aussavavirojekul P.Phimsiri H.Sripo T.Boonnag C.Tongdee T.Siriapisith T.Saiviroonporn P.Kinchagawat J.Ittichaiwong P.Mahidol University2024-09-282024-09-282024-01-01BioNLP 2024 - 23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, Proceedings of the Workshop and Shared Tasks (2024) , 635-644https://repository.li.mahidol.ac.th/handle/20.500.14594/101397Radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Our solution employs a lightweight multimodal language model (MLLM) enhanced with a two-stage post-processing strategy, utilizing a Large Language Model (LLM) to boost diagnostic accuracy and ensure patient safety. We introduce the "First, Do No Harm" SafetyNet, which incorporates X-Raydar, an advanced X-ray classification model, to cross-verify the model outputs and specifically address false negative errors from the MLLM. This comprehensive approach combines the efficiency of lightweight models with the robustness of thorough post-processing techniques, offering a reliable solution for radiology report generation. Our system achieved fourth place on the F1-Radgraph metric for findings generation in the Radiology Report Generation Shared Task (RRG24).1,..Computer ScienceMedicineEngineeringArts and HumanitiesConference PaperSCOPUS2-s2.0-85204496103