Update citation.

README.md

@@ -14,10 +14,10 @@ Findings”.
 | **Languages** | English |
 | **License** | [![Model License](https://img.shields.io/badge/License-Microsoft%20Research-red)](LICENSE) |
 | **Data** | [![Data](https://img.shields.io/badge/Dataset-Annotations-228B22)](https://physionet.org/content/llava-rad-mimic-cxr-annotation/1.0.0/) |
-| **Code** | [![Code](https://img.shields.io/badge/GitHub-LLaVARad-FFA500)](https://github.com/microsoft/llava-rad) |
+| **Code** | [![Code](https://img.shields.io/badge/GitHub-LLaVA--Rad-FFA500)](https://github.com/microsoft/llava-rad) |
 | **Evaluation** | [![Eval](https://img.shields.io/badge/GitHub-CheXprompt-purple)](https://github.com/microsoft/chexprompt) |
-| **Preprint** | [![Preprint](https://img.shields.io/badge/Paper-arXiv-blue)](https://arxiv.org/abs/2403.08002) |
-| **Peer Reviewed Paper** | [![Paper](https://img.shields.io/badge/status-In%20Press-cyan)]() |
+| **Preprint** | [![Preprint](https://img.shields.io/badge/Preprint-arXiv-blue)](https://arxiv.org/abs/2403.08002) |
+| **Peer Reviewed Paper** | [![Paper](https://img.shields.io/badge/Paper-Nature%20Communications-cyan)](https://doi.org/10.1038/s41467-025-58344-x) |
 
 
 LlaVA-Rad is a 7 billion parameter small multimodal model trained to produce findings given an input chest X-ray. Its architecture follows that of [LLaVA](https://arxiv.org/abs/2310.03744) and [LLaVA-Med](https://arxiv.org/abs/2306.00890), differing in the use of a specialized chest X-ray image encoder, BiomedCLIP-CXR, built with the [BiomedCLIP](https://arxiv.org/abs/2303.00915) framework. LLaVA-Rad offers outstanding performance at relatively small model size.