Updates for shared healthcare docs in AI studio and ML studio

Author: msakande

articles/ai-studio/how-to/healthcare-ai/deploy-cxrreportgen.md

@@ -37,20 +37,19 @@ The CXRReportGen model combines a radiology-specific image encoder with a large
 
 ## Prerequisites
 
-[!INCLUDE [shared-ai-studio-and-azure-ml-articles](../../includes/shared-ai-studio-and-azure-ml-articles.md)]
-
-To use CXRReportGen model with Azure AI Studio or Azure Machine Learning studio, you need the following prerequisites:
+To use the CXRReportGen model, you need the following prerequisites:
 
 ### A model deployment
 
 **Deployment to a self-hosted managed compute**
 
-CXRReportGen model can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served. You can deploy the model through the model catalog UI or programmatically.
+CXRReportGen model can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served. You can deploy the model through the catalog UI (in [AI Studio](https://aka.ms/healthcaremodelstudio) or [Azure Machine Learning studio](https://ml.azure.com/model/catalog)) or deploy programmatically.
 
 To __deploy the model through the UI__:
 
-1. Go to the [model card in the catalog](https://aka.ms/cxrreportgenmodelcard). 
-1. On the model's overview page, select __Deploy__. 
+1. Go to the catalog.
+1. Search for _CxrReportGen_ and select the model card.
+1. On the model's overview page, select __Deploy__.
 1. If given the option to choose between serverless API deployment and deployment using a managed compute, select **Managed Compute**.
 1. Fill out the details in the deployment window.
 

articles/ai-studio/how-to/healthcare-ai/deploy-medimageinsight.md

@@ -35,19 +35,18 @@ An embedding model is capable of serving as the basis of many different solution
 
 ## Prerequisites
 
-[!INCLUDE [shared-ai-studio-and-azure-ml-articles](../../includes/shared-ai-studio-and-azure-ml-articles.md)]
-
-To use MedImageInsight models with Azure AI Studio or Azure Machine Learning studio, you need the following prerequisites:
+To use the MedImageInsight model, you need the following prerequisites:
 
 ### A model deployment
 
 **Deployment to a self-hosted managed compute**
 
-MedImageInsight model can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served. You can deploy the model through the model catalog UI or programmatically.
+MedImageInsight model can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served. You can deploy the model through the catalog UI (in [AI Studio](https://aka.ms/healthcaremodelstudio) or [Azure Machine Learning studio](https://ml.azure.com/model/catalog)) or deploy programmatically.
 
 To __deploy the model through the UI__:
 
-1. Go to the [model card in the catalog](https://aka.ms/mi2modelcard). 
+1. Go to the catalog.
+1. Search for _MedImageInsight_ and select the model card.
 1. On the model's overview page, select __Deploy__. 
 1. If given the option to choose between serverless API deployment and deployment using a managed compute, select **Managed Compute**.
 1. Fill out the details in the deployment window.

articles/ai-studio/how-to/healthcare-ai/deploy-medimageparse.md

@@ -38,20 +38,19 @@ Remarkably, the segmentation masks and textual descriptions were achieved by usi
 
 ## Prerequisites
 
-[!INCLUDE [shared-ai-studio-and-azure-ml-articles](../../includes/shared-ai-studio-and-azure-ml-articles.md)]
-
-To use MedImageParse model with Azure AI Studio or Azure Machine Learning studio, you need the following prerequisites:
+To use the MedImageParse model, you need the following prerequisites:
 
 ### A model deployment
 
 **Deployment to a self-hosted managed compute**
 
-MedImageParse model can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served. You can deploy the model through the model catalog UI or programmatically. 
+MedImageParse model can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served. You can deploy the model through the catalog UI (in [AI Studio](https://aka.ms/healthcaremodelstudio) or [Azure Machine Learning studio](https://ml.azure.com/model/catalog)) or deploy programmatically.
 
 To __deploy the model through the UI__:
 
-1. Go to the [model card in the catalog](https://aka.ms/medimageparsemodelcard). 
-1. On the model's overview page, select __Deploy__. 
+1. Go to the catalog.
+1. Search for _MedImageParse_ and select the model card.
+1. On the model's overview page, select __Deploy__.
 1. If given the option to choose between serverless API deployment and deployment using a managed compute, select **Managed Compute**.
 1. Fill out the details in the deployment window.
 

articles/ai-studio/how-to/healthcare-ai/healthcare-ai-models.md

@@ -26,7 +26,7 @@ The healthcare industry is undergoing a revolutionary transformation driven by t
 
 :::image type="content" source="../../media/how-to/healthcare-ai/connect-modalities.gif" alt-text="Models that reason about various modalities come together to support discover, development and delivery of healthcare":::
 
-The [Azure AI model catalog](../model-catalog-overview.md) provides healthcare foundation models that facilitate AI-powered analysis of various medical data types and expand well beyond medical text comprehension into the multimodal reasoning about medical data. These AI models can integrate and analyze data from diverse sources that come in various modalities, such as medical imaging, genomics, clinical records, and other structured and unstructured data sources. The models also span several healthcare fields like dermatology, ophthalmology, radiology, and pathology. 
+The Azure AI model catalog available in [AI Studio](../model-catalog-overview.md) and [Azure Machine Learning studio](../../../machine-learning/concept-model-catalog.md) provides healthcare foundation models that facilitate AI-powered analysis of various medical data types and expand well beyond medical text comprehension into the multimodal reasoning about medical data. These AI models can integrate and analyze data from diverse sources that come in various modalities, such as medical imaging, genomics, clinical records, and other structured and unstructured data sources. The models also span several healthcare fields like dermatology, ophthalmology, radiology, and pathology. 
 
 [!INCLUDE [shared-ai-studio-and-azure-ml-articles](../../includes/shared-ai-studio-and-azure-ml-articles.md)]