  Radiology reporting is a complex task that requires detailed image
understanding, integration of multiple inputs, including comparison with prior
imaging, and precise language generation. This makes it ideal for the
development and use of generative multimodal models. Here, we extend report
generation to include the localisation of individual findings on the image - a
task we call grounded report generation. Prior work indicates that grounding is
important for clarifying image understanding and interpreting AI-generated
text. Therefore, grounded reporting stands to improve the utility and
transparency of automated report drafting. To enable evaluation of grounded
reporting, we propose a novel evaluation framework - RadFact - leveraging the
reasoning capabilities of large language models (LLMs). RadFact assesses the
factuality of individual generated sentences, as well as correctness of
generated spatial localisations when present. We introduce MAIRA-2, a large
multimodal model combining a radiology-specific image encoder with a LLM, and
trained for the new task of grounded report generation on chest X-rays. MAIRA-2
uses more comprehensive inputs than explored previously: the current frontal
image, the current lateral image, the prior frontal image and prior report, as
well as the Indication, Technique and Comparison sections of the current
report. We demonstrate that these additions significantly improve report
quality and reduce hallucinations, establishing a new state of the art on
findings generation (without grounding) on MIMIC-CXR while demonstrating the
feasibility of grounded reporting as a novel and richer task.
