LLM-based Automatic Medical Imaging Report Generation and Evaluation Toolkit

This project is a GitHub open-source toolkit (Author: jinghanSunn, Link: https://github.com/jinghanSunn/LLM-based-Radiology-Report-Generation-Evaluation-Toolkit). Its core goal is to use large language models (LLMs) to realize automated radiology report generation for chest X-ray images, and provide multi-dimensional clinical and natural language generation (NLG) evaluation metrics, offering an out-of-the-box solution for researchers and developers.

Traditional radiology report writing requires professional physicians to spend a lot of time, and deep learning-based automated report generation technology can significantly improve efficiency. In recent years, the strong capabilities of LLMs in natural language understanding and generation have brought new technical paths for medical imaging report generation.

1. Report Generation Module: Combines image features extracted by computer vision models with the language generation ability of LLMs to output structured diagnostic reports that comply with clinical standards; 2. Multi-dimensional Evaluation: Clinical metrics (pathological detection accuracy evaluated via CheXbert) + NLG metrics (BLEU, ROUGE, METEOR to measure fluency and similarity to reference texts); 3. LLM Annotator: Provides scripts to support LLMs as automatic annotation tools, reducing the cost of manual evaluation.

The toolkit is developed in Python, with main dependencies including LLM interfaces (supports OpenAI GPT, open-source LLMs, etc.), CheXbert (medical entity recognition and pathology classification), and standard NLG evaluation libraries. It has a clear code structure with independent evaluation scripts and modular design, facilitating customization and expansion.

Applicable to: 1. Medical imaging AI research (standardized report generation and evaluation benchmarks); 2. Clinical auxiliary diagnosis (provides preliminary report drafts for radiologists); 3. Model performance comparison (supports fair comparison of different LLM models); 4. Medical education (trains medical students to understand report structure and terminology).

Practical Significance: Alleviates the shortage of radiologists under uneven distribution of medical resources, promotes the standardized development of medical imaging AI technology, and ensures the quality and safety of generated reports. Outlook: With the advancement of multi-modal LLM technology, more accurate and reliable automated medical report generation systems will be realized in the future, providing strong support for clinical diagnosis.