XRayBench is a state-of-the-art evaluation platform designed specifically for assessing the performance of large language models (LLMs) in the domain of medical X-ray image analysis. By harnessing the power of cutting-edge AI, XRayBench provides a comprehensive framework for testing and refining LLMs across multiple tasks, ensuring they meet the highest standards in radiological diagnostics.
-
End-to-End Analysis Workflow: XRayBench evaluates LLMs on the entire X-ray analysis pipeline, from image preprocessing and anomaly detection to diagnosis generation and report creation. The suite supports real-world medical scenarios to ensure that LLMs are capable of making accurate and clinically relevant decisions.
-
Multi-Agent Collaboration: XRayBench allows LLMs to collaborate with specialized agents designed for feature extraction, anomaly detection, and clinical reporting. This unique collaborative environment simulates the real-world workflow of radiologists and AI models working together.
-
Performance Metrics: XRayBench provides a detailed evaluation using industry-standard metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. The suite also measures time-to-diagnosis and model interpretability, critical for clinical adoption.
-
Dynamic Dataset Integration: Utilizing vast open-source X-ray datasets such as NIH ChestX-ray14 and MURA, XRayBench ensures a rich and diverse set of test cases. The suite is designed for flexibility, allowing users to integrate their custom datasets for tailored evaluations.
-
Customizable Evaluation Pipelines: XRayBench allows users to configure custom pipelines for specific evaluation needs, including benchmarking different LLMs across various medical imaging tasks, fine-tuning workflows, and testing specific diagnostic features.
-
Clinical-Grade Reports: Generate fully automated, human-readable diagnostic reports with real-time validation against medical labels. XRayBench ensures that LLMs produce accurate, detailed, and structured diagnostic outputs.
-
Scalable and Cloud-Ready: Designed with scalability in mind, XRayBench supports large-scale testing and training in cloud environments. From single-image evaluations to processing thousands of images, XRayBench ensures seamless performance at any scale.
- AI Researchers: Benchmark the performance of novel LLM architectures for medical imaging tasks.
- Radiologists: Leverage XRayBench for validating AI tools that assist in clinical diagnosis, ensuring models perform reliably and accurately in high-stakes environments.
- Healthcare Organizations: Integrate AI into radiology workflows with confidence, using XRayBench to evaluate and select the best-performing models for clinical deployment.
- AI Developers: Fine-tune models and rapidly assess their clinical applicability in X-ray diagnostics.
XRayBench is your go-to platform for pushing the boundaries of AI in medical imaging. Empower your models to meet clinical standards with unmatched accuracy, explainability, and efficiency.
This project is licensed under the MIT License. See the LICENSE file for more details.