Update README.md

README.md

@@ -7,14 +7,14 @@ language:
 tags:
 - medical
 - diagnosis
-pretty_name: OpenDxBench
+pretty_name: MedReaMM
 size_categories:
 - n<1K
 ---
-# Dataset Card for OpenDxBench
-OpenDxBench is a **multimodal medical diagnostic reasoning benchmark** designed to evaluate the diagnostic capabilities of Large Language Models (LLMs) and Large Multimodal Models (LMMs) in real-world clinical scenarios. The benchmark dataset is collected from top-tier medical journals and curated clinical case report databases, and includes 625 expert-validated real-world cases.
+# Dataset Card for MedReaMM
+MedReaMM is a **multimodal medical diagnostic reasoning benchmark** designed to evaluate the diagnostic capabilities of Large Language Models (LLMs) and Large Multimodal Models (LMMs) in real-world clinical scenarios. The benchmark dataset is collected from top-tier medical journals and curated clinical case report databases, and includes 625 expert-validated real-world cases.
 
-Unlike existing benchmarks that primarily rely on textual modality, OpenDxBench includes a large number of medical images (such as CT, X-ray, MRI, etc.), with an average of 2.79 images per case, totaling 1746 images. Each case in the dataset is provided with detailed patient information, images and their descriptions (captions), and standardized diagnoses annotated with ICD-11 codes.
+Unlike existing benchmarks that primarily rely on textual modality, MedReaMM includes a large number of medical images (such as CT, X-ray, MRI, etc.), with an average of 2.79 images per case, totaling 1746 images. Each case in the dataset is provided with detailed patient information, images and their descriptions (captions), and standardized diagnoses annotated with ICD-11 codes.
 
 This dataset aims to bridge the gap between text-based benchmarks and real-world clinical practice, providing a more comprehensive platform for evaluating and developing more powerful medical AI models.