{"type":"ScholarlyArticle","title":"PDD-RRG: Posterior Diagnostic Decision for Study-level Radiology Report Generation","authors":[{"name":"Yang Yu","slug":"yang-yu","given":"Yang","family":"Yu","affiliation":""},{"name":"Yiming Ji","slug":"yiming-ji","given":"Yiming","family":"Ji","affiliation":""},{"name":"Bin Dai","slug":"bin-dai","given":"Bin","family":"Dai","affiliation":""},{"name":"Dong Zhang","slug":"dong-zhang","given":"Dong","family":"Zhang","affiliation":""},{"name":"Zhiyong Zhou","slug":"zhiyong-zhou","given":"Zhiyong","family":"Zhou","affiliation":""},{"name":"Shoushan Li","slug":"shoushan-li","given":"Shoushan","family":"Li","affiliation":""},{"name":"Yakang Dai","slug":"yakang-dai","given":"Yakang","family":"Dai","affiliation":""}],"abstract":"Automatic radiology report generation (RRG) aims to simulate the workflow of radiologists, assisting them in clinical diagnosis. However, existing methods often fall short in utilizing all information relevant to the examination, as is typically done in clinical practice. Although some works attempt to incorporate multi-view images and historical data, these additional inputs may sometimes lead to avoidable diagnostic errors on the contrary. To address these challenges, we introduce a decision-making stage after report generation for the first time and propose a Posterior Diagnostic Decision framework (PDD-RRG) to integrate potentially conflicting diagnoses. Specifically, we create various subsets of input data and utilize an existing RRG model to generate reports from different perspectives. Then the Bayesian posterior probability and the learned thresholds for each clinical observation are calculated to obtain an aggregated diagnostic conclusion, which is subsequently used to refine the generated report. Experiments on MIMIC-CXR demonstrate that our proposed PDD-RRG can effectively enhance the clinical efficacy of existing RRG models without any retraining.","doi":"10.24963/ijcai.2026/229","url":"https://ijcai.org/proceedings/2026/229","pdf":"https://ijcai.org/proceedings/2026/0229.pdf","markdown":"https://ijcai.org/proceedings/2026/229.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/229","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":229,"pages":{"first":2055,"last":2063},"keywords":["Multidisciplinary Topics and Applications: Health and medicine","Computer Vision: Biomedical image analysis","Multidisciplinary Topics and Applications: Bioinformatics"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}