CXR-LLAVA: a multimodal large language model for interpreting chest X-ray images

TL;DR

CXR-LLAVA is an open-source multimodal large language model designed for interpreting chest X-ray images, demonstrating competitive diagnostic performance and potential for autonomous radiology reporting, thus advancing AI applications in medical imaging.

Contribution

This work introduces CXR-LLAVA, a novel multimodal LLM trained on extensive CXR datasets, integrating vision transformers with language models for improved radiology interpretation.

Findings

Achieved an average F1 score of 0.81 on internal test set

Surpassed GPT-4-vision and Gemini-Pro-Vision in performance

Achieved 72.7% success rate in autonomous reporting

Abstract

Purpose: This study aimed to develop an open-source multimodal large language model (CXR-LLAVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists Materials and Methods: For training, we collected 592,580 publicly available CXRs, of which 374,881 had labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided free-text radiology reports (Dataset 2). After pre-training a vision transformer with Dataset 1, we integrated it with an LLM influenced by the LLAVA network. Then, the model was fine-tuned, primarily using Dataset 2. The model's diagnostic performance for major pathological findings was evaluated, along with the acceptability of radiologic reports by human radiologists, to gauge its potential for autonomous reporting. Results: The model demonstrated impressive performance in test sets, achieving an average F1 score of 0.81 for six major pathological findings in the MIMIC internal test set and 0.62 for seven major pathological findings in the external test set. The model's F1 scores surpassed those of GPT-4-vision and Gemini-Pro-Vision in both test sets. In human radiologist evaluations of the external test set, the model achieved a 72.7% success rate in autonomous reporting, slightly below the 84.0% rate of ground truth reports. Conclusion: This study highlights the significant potential of multimodal LLMs for CXR interpretation, while also acknowledging the performance limitations. Despite these challenges, we believe that making our model open-source will catalyze further research, expanding its effectiveness and applicability in various clinical contexts. CXR-LLAVA is available at https://github.com/ECOFRI/CXR_LLAVA.