Updated README.md

README.md

@@ -92,17 +92,6 @@ Qure has been evaluated using both standard NLP benchmarks and specific medical
 | Preliminary Diagnosis      | **Specificity** | 87.4%  | Yes       |
 | Preliminary Diagnosis      | **F1-Score**    | 89.8%  | Yes       |
 
-### **Competitions**
-
-Qure has participated in and excelled at several prestigious AI and medical competitions, showcasing its strength in handling complex medical data and language tasks.
-
-| Competition Name   | Metric   | Value  | Rank      |
-|--------------------|----------|--------|-----------|
-| **AI for Healthcare Challenge** | **Accuracy** | 88.2%  | 3rd       |
-| **Medical NLP Task at MedAI**   | **ROUGE-L** | 45.0   | 2nd       |
-| **Image-based Diagnosis Challenge** | **AUC** | 95.5%  | 1st       |
-| **Radiology AI Competition** | **F1-Score** | 89.0%  | 2nd       |
-
 ### Model Efficiency
 - **Training Time**: 15 hours for fine-tuning on a medical dataset of 50,000 samples (depending on the hardware used).
 - **Inference Latency**: ~300ms per sample on a single A100 GPU for text analysis, and ~500ms for image analysis.