{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/maira-2-grounded-radiology-report-generation","title":"MAIRA-2: Grounded Radiology Report Generation","arxiv_id":"2406.04449","date":"2024-06-06","proceeding":null,"authors":["Shruthi Bannur","Kenza Bouzid","Daniel C. Castro","Anton Schwaighofer","Anja Thieme","Sam Bond-Taylor","Maximilian Ilse","Fernando Pérez-García","Valentina Salvatelli","Harshita Sharma","Felix Meissen","Mercy Ranjit","Shaury Srivastav","Julia Gong","Noel C. F. Codella","Fabian Falck","Ozan Oktay","Matthew P. Lungren","Maria Teodora Wetscherek","Javier Alvarez-Valle","Stephanie L. Hyland"],"abstract":"Radiology reporting is a complex task requiring detailed medical image understanding and precise language generation, for which generative multimodal models offer a promising solution. However, to impact clinical practice, models must achieve a high level of both verifiable performance and utility. We augment the utility of automated report generation by incorporating localisation of individual findings on the image - a task we call grounded report generation - and enhance performance by incorporating realistic reporting context as inputs. We design a novel evaluation framework (RadFact) leveraging the logical inference capabilities of large language models (LLMs) to quantify report correctness and completeness at the level of individual sentences, while supporting the new task of grounded reporting. We develop MAIRA-2, a large radiology-specific multimodal model designed to generate chest X-ray reports with and without grounding. MAIRA-2 achieves state of the art on existing report generation benchmarks and establishes the novel task of grounded report generation.","url_abs":"https://arxiv.org/abs/2406.04449v2","url_pdf":"https://arxiv.org/pdf/2406.04449v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"maira-2-grounded-radiology-report-generation","repo_url":"https://github.com/microsoft/RadFact","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2406.04449","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.04449"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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