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Vision-Language Model for Generating Textual Descriptions From Clinical Images: Model Development and Validation

Jia Ji¹, Yongshuai Hou², Xinyu Chen³

¹Shenzhen Institute of Information Technology, Shenzhen, China.

JMIR Formative Research

|February 8, 2024

Summary

This study introduces ClinicalBLIP, an AI model for automatic radiology report generation. ClinicalBLIP significantly improves report quality by integrating advanced vision-language models for better image interpretation and text creation.

Keywords:

clinical image multistage fine-tuning prior knowledge radiology report generation vision-language model

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Area of Science:

Artificial Intelligence in Medicine
Medical Imaging Analysis
Natural Language Processing

Background:

Automatic generation of radiology reports is crucial for healthcare standardization.
AI and natural language processing can accelerate report creation.
Existing methods often underutilize advanced vision-language models.

Purpose of the Study:

To integrate pretrained vision-language models for automatic radiology report generation.
To convert clinical images into high-quality textual reports using AI.

Main Methods:

Developed ClinicalBLIP, based on InstructBLIP, fine-tuned on clinical image-to-text datasets.
Employed a multistage fine-tuning approach with low-rank adaptation for enhanced comprehension.
Integrated prior knowledge via prompt learning to improve report precision.

Main Results:

ClinicalBLIP achieved superior METEOR and ROUGE scores on IU X-RAY and MIMIC-CXR datasets.
Performance surpassed existing state-of-the-art methods.
Multistage fine-tuning and prior information integration significantly improved results.

Conclusions:

ClinicalBLIP demonstrates robust and effective clinical radiology report generation.
The model shows significant promise for real-world clinical applications.
AI-driven report generation enhances healthcare quality and standardization.