A clinically accessible small multimodal radiology model and evaluation metric for chest X-ray findings.

ABSTRACT: Large foundation models show promise in biomedicine but face challenges in clinical use due to performance gaps, accessibility, cost, and lack of scalable evaluation. Here we show that open-source small multimodal models can bridge these gaps in radiology by generating free-text findings from chest X-ray images. Our data-centric approach leverages 697K curated radiology image-text pairs to train a specialized, domain-adapted chest X-ray encoder. We integrate this encoder with pre-trained language models via a lightweight adapter that aligns image and text modalities. To enable robust, clinically relevant evaluation, we develop and validate CheXprompt, a GPT-4-based metric for assessing factual accuracy aligned with radiologists' evaluations. Benchmarked with CheXprompt and other standard f

SUBMITTER: Zambrano Chaves JM 

PROVIDER: S-EPMC11962106 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature