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In this study, we aim to initiate the development of Radiology FoundationModel, termed as RadFM. We consider the construction of foundational modelsfrom three perspectives, namely, dataset construction, model design, andthorough evaluation. Our contribution can be concluded as follows: (i), weconstruct a large-scale Medical Multi-modal Dataset, MedMD, which consists of16M 2D and 3D medical scans with high-quality text descriptions or reportsacross various data formats, modalities, and tasks, covering over 5000 distinctdiseases. To the best of our knowledge, this is the first large-scale,high-quality, medical visual-language dataset, with both 2D and 3D scans; (ii),we propose an architecture that enables visually conditioned generativepre-training, i.e., allowing for integration of text input with 2D or 3Dmedical scans, and generate responses for diverse radiologic tasks. The modelwas initially pre-trained on MedMD and subsequently fine-tuned on thedomain-specific dataset, which is a radiologic cleaned version of MedMD,containing 3M radiologic visual-language pairs, termed as RadMD; (iii), wepropose a new evaluation benchmark, RadBench, that comprises five tasks,including modality recognition, disease diagnosis, visual question answering,report generation and rationale diagnosis, aiming to comprehensively assess thecapability of foundation models in handling practical clinical problems. Weconduct both automatic and human evaluation on RadBench, in both cases, RadFMoutperforms existing multi-modal foundation models, that are publicalyaccessible, including Openflamingo, MedFlamingo, MedVInT and GPT-4V.Additionally, we also adapt RadFM for different public benchmarks, surpassingexisting SOTAs on diverse datasets. All codes, data, and model checkpoint willall be made publicly available to promote further research and development inthe field.

Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data