Update README.md

README.md

@@ -26,11 +26,22 @@ base_model: microsoft/MIRA
 
 ## Model Summary
 
-This repository hosts the **Large-scale configuration** of the open-source demonstration for **MIRA: Medical Time Series Foundation Model for Real-World Health Data**.
+This repository hosts the **Large-scale configuration** of the **official open-source release** of **MIRA: Medical Time Series Foundation Model for Real-World Health Data**.
 
-The goal of this release is to offer a **fully accessible and reproducible version** of the MIRA framework. Unlike the internal version described in the paper which utilizes private clinical data, this model was trained **exclusively on publicly available time-series datasets**. It is designed to showcase the core architectural innovations of MIRA—such as CT-RoPE and Frequency-Specialized MoE—without compromising patient privacy.
+MIRA addresses the critical challenges of real-world health data—irregular sampling, missing values, and complex temporal dynamics—by combining the scalability of Transformers with the continuous-time modeling capabilities of Neural ODEs. This release provides the fully accessible weights for the large model architecture, offering researchers and practitioners a powerful tool for developing next-generation clinical AI applications.
 
-**Disclaimer:** This is a demonstration reproduction. While it shares the same "Large" architecture and mechanisms as the original MIRA, its performance on downstream clinical tasks may differ due to the restriction to public training data.
+### Key Capabilities
+* **SOTA Performance:** Outperforms existing baselines on major public medical time-series benchmarks.
+* **Massive Scale:** Trained on extensive time-series datasets to ensure generalization across different patient demographics and conditions.
+* **Robustness:** Natively handles missing data and irregular timestamps without requiring complex imputation preprocessing.
+
+---
+
+## Data Privacy & Compliance
+
+To ensure strict adherence to global privacy regulations (such as HIPAA and GDPR) and to facilitate unrestricted open-source adoption, this model was trained **exclusively on publicly available time series datasets**.
+
+**Note:** This model does not contain or memorize any private, non-public patient health information (PHI). It serves as a privacy-safe foundation model suitable for benchmarking, research, and downstream fine-tuning on secure internal data. While it shares the same "Large" architecture and mechanisms as the original MIRA, its performance on downstream clinical tasks may differ due to the restriction to public training data.
 
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