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README.md

@@ -47,6 +47,14 @@ pipeline_tag: sentence-similarity
 
 **CardioEmbed** is a domain-specialized embedding model fine-tuned on comprehensive cardiology textbooks for clinical applications. Built on [Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) using LoRA adapters, this model achieves **state-of-the-art performance** on biomedical retrieval tasks while maintaining efficiency through 8-bit quantization.
 
+### Why CardioEmbed?
+
+Cardiovascular disease remains the **leading cause of death globally**, accounting for approximately **18 million deaths annually** and representing nearly one-third of all mortality worldwide. In the United States alone, cardiovascular disease imposes an estimated annual economic burden exceeding **$400 billion** in direct medical costs and lost productivity.
+
+As machine learning systems increasingly support clinical decision-making in cardiology—from risk stratification and diagnostic assistance to treatment optimization—the quality of semantic text representations becomes critical. However, existing biomedical embedding models trained primarily on PubMed research literature may not fully capture the **procedural knowledge and specialized terminology** found in clinical cardiology textbooks that practitioners actually use.
+
+**CardioEmbed bridges this research-practice gap** by training on comprehensive cardiology textbooks, achieving near-perfect retrieval accuracy on cardiac-specific tasks while maintaining strong performance on general biomedical benchmarks.
+
 ### Key Features
 
 - 🏥 **Medical Domain Expertise**: Trained on 106,432 cardiology-specific sentence pairs from authoritative textbooks
@@ -65,6 +73,14 @@ pipeline_tag: sentence-similarity
 
 *MRR@10 on biomedical retrieval tasks. See [paper](https://arxiv.org/abs/XXXX.XXXXX) for full results.*
 
+### Performance Visualization
+
+CardioEmbed achieves **99.60% Acc@1** on cardiac-specific retrieval, outperforming MedTE (current SOTA medical embedding) by **+15.94 percentage points**:
+
+![Model Comparison](https://raw.githubusercontent.com/ricyoung/CardioEmbed/master/Final_Published_Paper/figures/figure1_model_comparison.png)
+
+*Figure: Comparison of CardioEmbed against state-of-the-art medical and general-purpose embedding models on cardiology retrieval tasks.*
+
 ---
 
 ## Quick Start
@@ -298,7 +314,9 @@ For questions or issues, please open an issue on [GitHub](https://github.com/ric
 
 **Built with ❤️ for advancing medical AI research**
 
-*Research by [Richard J. Young](https://huggingface.co/richardyoung) & Alice M. Matthews | [DeepNeuro.AI](https://deepneuro.ai)*
+*By [Richard J. Young](https://deepneuro.ai) & Alice M. Matthews*
+
+[![DeepNeuro.AI](https://img.shields.io/badge/🧠-DeepNeuro.AI-orange)](https://deepneuro.ai)
 
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