About the Platform
Multimodal Medical AI Benchmark
A unified platform to evaluate, compare, and understand the performance of AI models in real-world medical diagnosis scenarios.
🎯 Purpose
The goal of this platform is to provide a standardized benchmark for evaluating multimodal AI models in healthcare. It enables fair comparison across models and promotes transparency in performance.
📊 Dataset
The dataset is divided into multiple difficulty levels (Easy, Medium, Hard) to simulate real clinical complexity. It includes multimodal inputs such as text, imaging, and structured data.
⚙️ Evaluation
Models are evaluated using metrics like Pass@1, Pass@5, and semantic similarity scores. These metrics measure both accuracy and reasoning capability in diagnostic tasks.
🏥 Organization
This platform is developed in collaboration with UTH to advance research in AI-powered medical diagnostics and support clinical decision-making.
Why This Matters
As AI becomes more integrated into healthcare, it is critical to evaluate models in realistic scenarios. This platform helps bridge the gap between research and clinical application by providing measurable, comparable results.