Med_Benchmarks_LLMs: An Automated Benchmarking Framework for Evaluating Medical Large Language Models

Med_Benchmarks_LLMs is an automated benchmarking framework for evaluating medical large language models, designed to address the fragmentation issue in medical AI evaluation. It systematically collects medical benchmark data (covering text and multimodal categories) from Hugging Face and GitHub, processes it in a structured manner, provides a reliable basis for model selection in clinical scenarios, and lowers the threshold for researchers to access and use benchmark resources.

Medical AI has extremely high requirements for accuracy and reliability, but current evaluations face fragmentation issues: different teams use different datasets, metrics, and protocols, making it difficult to compare models and resulting in poor reproducibility. The root cause lies in the lack of unified and comprehensive benchmark resources, forcing researchers to spend a lot of time handling data formats and evaluation code. Med_Benchmarks_LLMs addresses this pain point through automated collection and standardized processing.

Core Feature Architecture

• One-stop resource library: Continuously monitors Hugging Face and GitHub to automatically identify new medical datasets and benchmarks
• Category support: Covers pure text (medical Q&A, medical record summarization, etc.) and multimodal (medical imaging, pathological slice analysis, etc.) benchmarks
• Data structuring: Converts various formats like JSON and CSV into a unified standard format

Technical Implementation Details

• Crawler and parsing system: Uses Hugging Face API to obtain data, and extracts information through GitHub repository analysis
• Data processing pipeline: Resource discovery → download verification → format conversion → quality check, ensuring data reliability and timeliness

Clinical Application Scenarios

• Medical education: Q&A benchmarks evaluate the knowledge reserve and expression ability of models as teaching assistants
• Clinical decision support: Diagnostic reasoning benchmarks test the accuracy and comprehensiveness of models' differential diagnoses based on symptoms/examination results
• Multimodal scenarios: Comprehensive judgment combining vision and text, such as image diagnosis and pathological analysis

Evaluation Methodology

• Targeted benchmark selection: Filter relevant subsets according to application scenarios
• Baseline comparison: Compare with known models to determine relative advantages
• Manual verification: Involve professional physicians to review key benchmarks to ensure results align with clinical practice

• Privacy compliance: Prioritize public, desensitized datasets to avoid real patient information
• Data quality: Ensure accuracy through multi-source cross-validation and expert review
• Fairness: Emphasize data diversity to avoid biases in evaluation results across different populations/disease spectrums

Practical Usage Guide

• Clarify evaluation objectives and filter corresponding benchmark subsets
• Download preprocessed data via automated tools or repositories and sync updates regularly
• Implement evaluations with reference to sample code and best practices

Future Development Directions

• Support more languages/regional medical data
• Introduce dynamic evaluation mechanisms to test continuous learning capabilities
• Enhance multimodal benchmark coverage (video, time-series medical data)
• Encourage community contributions of new datasets and improvement suggestions