@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 11/19/2024
+ms.date: 02/12/2025
 ms.author: nitinme
 monikerRange: 'doc-intel-4.0.0'
 ---
@@ -15,7 +15,7 @@ monikerRange: 'doc-intel-4.0.0'
 
 # Document Intelligence query field extraction
 
-**Document Intelligence now supports query field to extend the schema of any prebuilt model to extract the specific fields you need. Query fields can also be added to layout to extract fields in addition to structure from forms or documents.
+Document Intelligence now supports query field to extend the schema of any prebuilt model to extract the specific fields you need. Query fields can also be added to layout to extract fields in addition to structure from forms or documents.
 > [!NOTE]
 >
 > Document Intelligence Studio query field extraction is currently available with layout and prebuilt models, excluding the UX.Tax prebuilt models.
@@ -45,18 +45,18 @@ For query field extraction, specify the fields you want to extract and Document
 * In addition to the query fields, the response includes the model output. For a list of features or schema extracted by each model, see [model analysis features](../model-overview.md#model-analysis-features).
 
 
-## Query fields REST API request**
+## Query fields REST API request
 
-Use the query fields feature with the [general document model](../prebuilt/general-document.md), and add fields to the extraction process without having to train a custom model:
+Use the query fields feature with the [prebuilt layout](../prebuilt/layout.md) model, and add fields to the extraction process without having to train a custom model:
 
 ```http
-POST https://{endpoint}/documentintelligence/documentModels/prebuilt-layout:analyze?api-version=2024-02-29-preview&features=queryFields&queryFields=Terms,PaymentDate HTTP/1.1
+POST https://{endpoint}/documentintelligence/documentModels/prebuilt-layout:analyze?api-version=2024-11-30&features=queryFields&queryFields=OurReference,BookingDate HTTP/1.1
 Host: *.cognitiveservices.azure.com
 Content-Type: application/json
 Ocp-Apim-Subscription-Key:
 
 {
-  "urlSource": "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf"
+  "urlSource": "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/layout.png"
 }

@@ -192,7 +192,7 @@ analyzeLayoutResult.getPages().forEach(documentPage -> {
   documentPage.getLines().forEach(documentLine ->
     System.out.printf("Line %s is within a bounding polygon %s.%n",
       documentLine.getContent(),
-      documentLine.getBoundingPolygon().toString()));
+      documentLine.getPolygon().toString()));
 
   // words
   documentPage.getWords().forEach(documentWord ->
@@ -258,7 +258,7 @@ analyzeLayoutResult.getPages().forEach(documentPage -> {
   documentPage.getLines().forEach(documentLine ->
     System.out.printf("Line %s is within a bounding polygon %s.%n",
       documentLine.getContent(),
-      documentLine.getBoundingPolygon().toString()));
+      documentLine.getPolygon().toString()));
 
   // words
   documentPage.getWords().forEach(documentWord ->
@@ -270,7 +270,7 @@ analyzeLayoutResult.getPages().forEach(documentPage -> {
   documentPage.getSelectionMarks().forEach(documentSelectionMark ->
     System.out.printf("Selection mark is '%s' and is within a bounding polygon %s with confidence %.2f.%n",
       documentSelectionMark.getSelectionMarkState().toString(),
-      getBoundingCoordinates(documentSelectionMark.getBoundingPolygon()),
+      getBoundingCoordinates(documentSelectionMark.getPolygon()),
       documentSelectionMark.getConfidence()));
 });
 
@@ -290,8 +290,8 @@ for (int i = 0; i < tables.size(); i++) {
 }
 
 // Utility function to get the bounding polygon coordinates.
-private static String getBoundingCoordinates(List < Point > boundingPolygon) {
-  return boundingPolygon.stream().map(point -> String.format("[%.2f, %.2f]", point.getX(),
+private static String getBoundingCoordinates(List < Point > Polygon) {
+  return Polygon.stream().map(point -> String.format("[%.2f, %.2f]", point.getX(),
     point.getY())).collect(Collectors.joining(", "));
 }
 
@@ -351,7 +351,7 @@ analyzeResult.getPages().forEach(documentPage -> {
   documentPage.getLines().forEach(documentLine ->
     System.out.printf("Line %s is within a bounding polygon %s.%n",
       documentLine.getContent(),
-      documentLine.getBoundingPolygon().toString()));
+      documentLine.getPolygon().toString()));
 
   // words
   documentPage.getWords().forEach(documentWord ->

@@ -344,10 +344,10 @@ foreach (DocumentPage page in result.Pages)
         Console.WriteLine($"  Line {i} has content: '{line.Content}'.");
 
         Console.WriteLine($"    Its bounding box is:");
-        Console.WriteLine($"      Upper left => X: {line.BoundingPolygon[0].X}, Y= {line.BoundingPolygon[0].Y}");
-        Console.WriteLine($"      Upper right => X: {line.BoundingPolygon[1].X}, Y= {line.BoundingPolygon[1].Y}");
-        Console.WriteLine($"      Lower right => X: {line.BoundingPolygon[2].X}, Y= {line.BoundingPolygon[2].Y}");
-        Console.WriteLine($"      Lower left => X: {line.BoundingPolygon[3].X}, Y= {line.BoundingPolygon[3].Y}");
+        Console.WriteLine($"      Upper left => X: {line.Polygon[0].X}, Y= {line.Polygon[0].Y}");
+        Console.WriteLine($"      Upper right => X: {line.Polygon[1].X}, Y= {line.Polygon[1].Y}");
+        Console.WriteLine($"      Lower right => X: {line.Polygon[2].X}, Y= {line.Polygon[2].Y}");
+        Console.WriteLine($"      Lower left => X: {line.Polygon[3].X}, Y= {line.Polygon[3].Y}");
     }
 
     for (int i = 0; i < page.SelectionMarks.Count; i++)
@@ -356,10 +356,10 @@ foreach (DocumentPage page in result.Pages)
 
         Console.WriteLine($"  Selection Mark {i} is {selectionMark.State}.");
         Console.WriteLine($"    Its bounding box is:");
-        Console.WriteLine($"      Upper left => X: {selectionMark.BoundingPolygon[0].X}, Y= {selectionMark.BoundingPolygon[0].Y}");
-        Console.WriteLine($"      Upper right => X: {selectionMark.BoundingPolygon[1].X}, Y= {selectionMark.BoundingPolygon[1].Y}");
-        Console.WriteLine($"      Lower right => X: {selectionMark.BoundingPolygon[2].X}, Y= {selectionMark.BoundingPolygon[2].Y}");
-        Console.WriteLine($"      Lower left => X: {selectionMark.BoundingPolygon[3].X}, Y= {selectionMark.BoundingPolygon[3].Y}");
+        Console.WriteLine($"      Upper left => X: {selectionMark.Polygon[0].X}, Y= {selectionMark.Polygon[0].Y}");
+        Console.WriteLine($"      Upper right => X: {selectionMark.Polygon[1].X}, Y= {selectionMark.Polygon[1].Y}");
+        Console.WriteLine($"      Lower right => X: {selectionMark.Polygon[2].X}, Y= {selectionMark.Polygon[2].Y}");
+        Console.WriteLine($"      Lower left => X: {selectionMark.Polygon[3].X}, Y= {selectionMark.Polygon[3].Y}");
     }
 }
 
@@ -459,9 +459,9 @@ foreach (DocumentPage page in result.Pages)
 
         Console.WriteLine($"    Its bounding polygon (points ordered clockwise):");
 
-        for (int j = 0; j < line.BoundingPolygon.Count; j++)
+        for (int j = 0; j < line.Polygon.Count; j++)
         {
-            Console.WriteLine($"      Point {j} => X: {line.BoundingPolygon[j].X}, Y: {line.BoundingPolygon[j].Y}");
+            Console.WriteLine($"      Point {j} => X: {line.Polygon[j].X}, Y: {line.Polygon[j].Y}");
         }
     }
 
@@ -472,9 +472,9 @@ foreach (DocumentPage page in result.Pages)
         Console.WriteLine($"  Selection Mark {i} is {selectionMark.State}.");
         Console.WriteLine($"    Its bounding polygon (points ordered clockwise):");
 
-        for (int j = 0; j < selectionMark.BoundingPolygon.Count; j++)
+        for (int j = 0; j < selectionMark.Polygon.Count; j++)
         {
-            Console.WriteLine($"      Point {j} => X: {selectionMark.BoundingPolygon[j].X}, Y: {selectionMark.BoundingPolygon[j].Y}");
+            Console.WriteLine($"      Point {j} => X: {selectionMark.Polygon[j].X}, Y: {selectionMark.Polygon[j].Y}");
         }
     }
 }
@@ -542,9 +542,9 @@ foreach (DocumentPage page in result.Pages)
 
         Console.WriteLine($"    Its bounding polygon (points ordered clockwise):");
 
-        for (int j = 0; j < line.BoundingPolygon.Count; j++)
+        for (int j = 0; j < line.Polygon.Count; j++)
         {
-            Console.WriteLine($"      Point {j} => X: {line.BoundingPolygon[j].X}, Y: {line.BoundingPolygon[j].Y}");
+            Console.WriteLine($"      Point {j} => X: {line.Polygon[j].X}, Y: {line.Polygon[j].Y}");
         }
     }
 
@@ -555,9 +555,9 @@ foreach (DocumentPage page in result.Pages)
         Console.WriteLine($"  Selection Mark {i} is {selectionMark.State}.");
         Console.WriteLine($"    Its bounding polygon (points ordered clockwise):");
 
-        for (int j = 0; j < selectionMark.BoundingPolygon.Count; j++)
+        for (int j = 0; j < selectionMark.Polygon.Count; j++)
         {
-            Console.WriteLine($"      Point {j} => X: {selectionMark.BoundingPolygon[j].X}, Y: {selectionMark.BoundingPolygon[j].Y}");
+            Console.WriteLine($"      Point {j} => X: {selectionMark.Polygon[j].X}, Y: {selectionMark.Polygon[j].Y}");
         }
     }
 }

@@ -424,7 +424,7 @@ public class FormRecognizer {
       documentPage.getLines().forEach(documentLine ->
         System.out.printf("Line %s is within a bounding polygon %s.%n",
           documentLine.getContent(),
-          documentLine.getBoundingPolygon().toString()));
+          documentLine.getPolygon().toString()));
 
       // words
       documentPage.getWords().forEach(documentWord ->
@@ -436,7 +436,7 @@ public class FormRecognizer {
       documentPage.getSelectionMarks().forEach(documentSelectionMark ->
         System.out.printf("Selection mark is %s and is within a bounding polygon %s with confidence %.2f.%n",
           documentSelectionMark.getState().toString(),
-          documentSelectionMark.getBoundingPolygon().toString(),
+          documentSelectionMark.getPolygon().toString(),
           documentSelectionMark.getConfidence()));
     });
 
@@ -454,8 +454,8 @@ public class FormRecognizer {
     }
   }
   // Utility function to get the bounding polygon coordinates
-  private static String getBoundingCoordinates(List < Point > boundingPolygon) {
-    return boundingPolygon.stream().map(point -> String.format("[%.2f, %.2f]", point.getX(),
+  private static String getBoundingCoordinates(List < Point > Polygon) {
+    return Polygon.stream().map(point -> String.format("[%.2f, %.2f]", point.getX(),
       point.getY())).collect(Collectors.joining(", "));
   }
 }
@@ -556,7 +556,7 @@ public class FormRecognizer {
       documentPage.getLines().forEach(documentLine ->
         System.out.printf("Line '%s' is within a bounding polygon %s.%n",
           documentLine.getContent(),
-          getBoundingCoordinates(documentLine.getBoundingPolygon())));
+          getBoundingCoordinates(documentLine.getPolygon())));
 
       // words
       documentPage.getWords().forEach(documentWord ->
@@ -568,7 +568,7 @@ public class FormRecognizer {
       documentPage.getSelectionMarks().forEach(documentSelectionMark ->
         System.out.printf("Selection mark is '%s' and is within a bounding polygon %s with confidence %.2f.%n",
           documentSelectionMark.getSelectionMarkState().toString(),
-          getBoundingCoordinates(documentSelectionMark.getBoundingPolygon()),
+          getBoundingCoordinates(documentSelectionMark.getPolygon()),
           documentSelectionMark.getConfidence()));
     });
 
@@ -593,8 +593,8 @@ public class FormRecognizer {
   /**
    * Utility function to get the bounding polygon coordinates.
    */
-  private static String getBoundingCoordinates(List < Point > boundingPolygon) {
-    return boundingPolygon.stream().map(point -> String.format("[%.2f, %.2f]", point.getX(),
+  private static String getBoundingCoordinates(List < Point > Polygon) {
+    return Polygon.stream().map(point -> String.format("[%.2f, %.2f]", point.getX(),
       point.getY())).collect(Collectors.joining(", "));
   }
 }

@@ -17,7 +17,7 @@ To set the environment variable for your Language resource key, open a console w
 - To set the `LANGUAGE_KEY` environment variable, replace `your-key` with one of the keys for your resource.
 - To set the `LANGUAGE_ENDPOINT` environment variable, replace `your-endpoint` with the endpoint for your resource.
 
-[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
+[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/microsoft-entra-id-akv-expanded.md)]
 
 #### [Windows](#tab/windows)
 

@@ -191,12 +191,8 @@ Use this article to learn which natural languages are supported by the text PII,
 
 ## PII language support
 
-| Language              | Language code | Notes              |
-|-----------------------|---------------|--------------------|
-|German                 |`de`           |                    |
-|English                |`en`           |                    |
-|Spanish                |`es`           |                    |
-|French                 |`fr`           |                    |
+The Generally Available Conversational PII serivce currently supports English. Preview model version `2023-04-15-preview` supports English, German, Spanish, and French. 
+
 
 ---
 

@@ -6,7 +6,7 @@ manager: scottpolly
 keywords: Azure AI services, cognitive, Azure OpenAI
 ms.service: azure-ai-foundry
 ms.topic: overview
-ms.date: 11/04/2024
+ms.date: 02/13/2025
 ms.reviewer: shwinne
 ms.author: sgilley
 author: sdgilley

@@ -8,7 +8,7 @@ ms.service: azure-ai-foundry
 ms.custom:
   - ignite-2024
 ms.topic: concept-article #Don't change.
-ms.date: 11/18/2024
+ms.date: 02/13/2025
 #customer intent: As an admin, I want a central location where I can perform governance and management activities.
 ---
 

@@ -6,7 +6,7 @@ manager: scottpolly
 ms.service: azure-ai-foundry
 ms.custom: build-2024, devx-track-azurecli
 ms.topic: how-to
-ms.date: 11/21/2024
+ms.date: 02/13/2025
 ms.reviewer: dantaylo
 ms.author: sgilley
 author: sdgilley
@@ -78,27 +78,40 @@ az ml workspace create --kind hub --resource-group {my_resource_group} --name {m
 
 ## Create an AI Services connection
 
-After creating your own AI Services, you can connect it to your hub:
+After creating your own AI Services, you can connect it to your hub.
 
 # [Python SDK](#tab/python)
 
-```python
-from azure.ai.ml.entities import AzureAIServicesConnection
+1. Your `ml_client` connection now needs to include your hub:
 
-# constrict an AI Services connection
-my_connection_name = "myaiservivce"
-my_endpoint = "demo.endpoint" # this could also be called target
-my_api_keys = None # leave blank for Authentication type = AAD
-my_ai_services_resource_id = "" # ARM id required
+    * Provide your subscription details.  For `<AML_WORKSPACE_NAME>`, use your hub name:
+    
+        [!notebook-python[](~/azureml-examples-main/sdk/python/resources/connections/connections.ipynb?name=details)]
 
-my_connection = AzureAIServicesConnection(name=my_connection_name,
-                                    endpoint=my_endpoint, 
-                                    api_key= my_api_keys,
-                                    ai_services_resource_id=my_ai_services_resource_id)
+    * Get a handle to the hub:
 
-# Create the connection
-ml_client.connections.create_or_update(my_connection)
-```
+        [!notebook-python[](~/azureml-examples-main/sdk/python/resources/connections/connections.ipynb?name=ml_client)]
+
+2. Use `ml_client` to create the connection to your AI Services:
+
+    ```python
+    from azure.ai.ml.entities import AzureAIServicesConnection
+
+    # construct an AI Services connection
+    my_connection_name = "myaiservivce" # any name you want
+    aiservices_resource_name = <resource_name> # copy from Azure AI Foundry portal
+    my_endpoint = "<endpoint>" # copy from Azure AI Foundry portal
+    my_api_keys = None # leave blank for Authentication type = AAD
+    my_ai_services_resource_id = f"/subscriptions/{subscription_id}/resourceGroups/{resource_group}/providers/Microsoft.CognitiveServices/accounts/{aiservices_resource_name}"
+
+    my_connection = AzureAIServicesConnection(name=my_connection_name,
+                                        endpoint=my_endpoint, 
+                                        api_key= my_api_keys,
+                                        ai_services_resource_id=my_ai_services_resource_id)
+
+    # Create the connection
+    ml_client.connections.create_or_update(my_connection)
+    ```
 
 # [Azure CLI](#tab/azurecli)
 
@@ -113,26 +126,30 @@ You can use either an API key or credential-less YAML configuration file. For mo
     ```yml
     name: myazai_ei
     type: azure_ai_services
-    endpoint: https://contoso.cognitiveservices.azure.com/
+    endpoint: <endpoint for your AI Services>
     api_key: XXXXXXXXXXXXXXX
+    ai_services_resource_id: <fully_qualified_resource_id>
     ```
 
 - Credential-less
 
     ```yml    
     name: myazai_apk
     type: azure_ai_services
-    endpoint: https://contoso.cognitiveservices.azure.com/
+    endpoint: <endpoint for your AI Services>
+    ai_services_resource_id: <fully_qualified_resource_id>
     ```
 
+The <fully_qualified_resource_id> is the resource ID of your AI Services resource. It is in the format `/subscriptions/{subscription_id}/resourceGroups/{resource_group}/providers/Microsoft.CognitiveServices/accounts/{aiservices_resource_name}`.
+
 ---
 
 ## Create an Azure AI Foundry hub using existing dependency resources
 
 You can also create a hub using existing resources such as Azure Storage and Azure Key Vault. In the following examples, replace the example string values with your own values:
 
 > [!TIP]
-> You can retrieve the resource ID of the storage account and key vault from the Azure Portal by going to the resource's overview and selecting __JSON view__. The resource ID is located in the __id__ field. You can also use the Azure CLI to retrieve the resource ID. For example, `az storage account show --name {my_storage_account_name} --query "id"` and `az keyvault show --name {my_key_vault_name} --query "id"`.
+> You can retrieve the resource ID of the storage account and key vault from the Azure portal by going to the resource's overview and selecting __JSON view__. The resource ID is located in the __id__ field. You can also use the Azure CLI to retrieve the resource ID. For example, `az storage account show --name {my_storage_account_name} --query "id"` and `az keyvault show --name {my_key_vault_name} --query "id"`.
 
 # [Python SDK](#tab/python)
 

@@ -5,8 +5,8 @@ description: Learn about AI models that are applicable to the health and life sc
 ms.service: azure-ai-foundry
 manager: scottpolly
 ms.topic: concept-article
-ms.date: 10/20/2024
-ms.reviewer: itarapov
+ms.date: 02/13/2025
+ms.reviewer: franksolomon
 reviewer: ivantarapov
 ms.author: mopeakande
 author: msakande
@@ -18,30 +18,30 @@ author: msakande
 
 [!INCLUDE [health-ai-models-meddev-disclaimer](../../includes/health-ai-models-meddev-disclaimer.md)]
 
-In this article, you learn about Microsoft's catalog of multimodal healthcare foundation models. The models were developed in collaboration with Microsoft Research, strategic partners, and leading healthcare institutions for healthcare organizations. Healthcare organizations can use the models to rapidly build and deploy AI solutions tailored to their specific needs, while minimizing the extensive compute and data requirements typically associated with building multimodal models from scratch. The intention isn't for these models to serve as standalone products; rather, they're designed for developers to use as a foundation to build upon. With these healthcare AI models, professionals have the tools they need to harness the full potential of AI to enhance biomedical research, clinical workflows, and ultimately care delivery.
+In this article, you learn about the Microsoft multimodal healthcare foundation model catalog. The models were jointly developed by Microsoft Research, strategic partners, and leading healthcare institutions for healthcare organizations. Healthcare organizations can use the models to rapidly build and deploy AI solutions tailored to their specific needs, while minimizing the extensive compute and data requirements typically associated with building multimodal models from scratch. The intention isn't for these models to serve as standalone products. Instead, they're designed for developers to use as a foundation to build upon. With these healthcare AI models, professionals have the tools they need to harness the full potential of AI to enhance biomedical research, clinical workflows, and ultimately care delivery.
 
-The healthcare industry is undergoing a revolutionary transformation driven by the power of artificial intelligence (AI). While existing large language models like GPT-4 show tremendous promise for clinical text-based tasks and general-purpose multimodal reasoning, they struggle to understand non-text multimodal healthcare data such as medical imaging—radiology, pathology, ophthalmology—and other specialized medical text like longitudinal electronic medical records. They also find it challenging to process non-text modalities like signal data, genomic data, and protein data, much of which isn't publicly available.
+The power of artificial intelligence (AI) is driving a revolutionary transformation in the healthcare industry. While existing large language models like GPT-4 show tremendous promise for clinical text-based tasks and general-purpose multimodal reasoning, they struggle to understand non-text multimodal healthcare data - for example, medical imaging—radiology, pathology, and ophthalmology information resources. This problem covers other specialized medical text resources - for example, longitudinal electronic medical records. It becomes challenging to process non-text modalities like signal data, genomic data, and protein data, much of which isn't publicly available.
 
 :::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 available in [Azure AI Foundry](../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. 
+The Azure AI model catalog, available in [Azure AI Foundry](../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. These AI models expand well beyond medical text comprehension into the multimodal reasoning about medical data. They can integrate and analyze data from diverse sources that come in various modalities - for example, medical imaging, genomics, clinical records, and other structured and unstructured data sources. The models also span several healthcare fields, including dermatology, ophthalmology, radiology, pathology, and more.
 
 ## Microsoft first-party models
 
-The following models are Microsoft's first party multimodal healthcare foundation models.
+These models are Microsoft's first party multimodal healthcare foundation models.
 
 #### [MedImageInsight](./deploy-medimageinsight.md)
-This model is an embedding model that enables sophisticated image analysis, including classification and similarity search in medical imaging. Researchers can use the model embeddings in simple zero-shot classifiers or to build adapters for their specific tasks, thereby streamlining workflows in radiology, pathology, ophthalmology, dermatology, and other modalities. For example, researchers can explore how the model can be used to  build tools that automatically route imaging scans to specialists or flag potential abnormalities for further review. These actions can enable improved efficiency and patient outcomes. Furthermore, the model can be leveraged for Responsible AI (RAI) safeguards such as out-of-distribution (OOD) detection and drift monitoring, to maintain stability and reliability of AI tools and data pipelines in dynamic medical imaging environments.  
+This model is an embedding model that enables sophisticated image analysis, including classification and similarity search in medical imaging. Researchers can use the model embeddings in simple zero-shot classifiers. They can also build adapters for their specific tasks, thereby streamlining workflows in radiology, pathology, ophthalmology, dermatology, and other modalities. For example, researchers can use the model to build tools that automatically route imaging scans to specialists, or flag potential abnormalities for further review. These actions can boost efficiency and improve patient outcomes. Furthermore, the model supports Responsible AI (RAI) safeguards, such as out-of-distribution (OOD) detection and drift monitoring. These safeguards maintain the stability and reliability of AI tools and data pipelines in dynamic medical imaging environments.  
 
 #### [CXRReportGen](./deploy-cxrreportgen.md)
-Chest X-rays are the most common radiology procedure globally. They're crucial because they help doctors diagnose a wide range of conditions—from lung infections to heart problems. These images are often the first step in detecting health issues that affect millions of people. This multimodal AI model incorporates current and prior images along with key patient information to generate detailed, structured reports from chest X-rays. The reports highlight AI-generated findings directly on the images to align with human-in-the-loop workflows. Researchers can test this capability and the potential to accelerate turnaround times while enhancing the diagnostic precision of radiologists. 
+Chest X-rays are the most common radiology procedure globally. They help doctors diagnose a wide range of conditions - lung infections, heart problems, and more. For millions of people, these images often become the first step in detecting health issues. This multimodal AI model incorporates current and prior images, along with key patient information, to generate detailed, structured reports from chest X-rays. The reports highlight AI-generated findings based directly on the images, to align with human-in-the-loop workflows. Researchers can test this capability and the potential to accelerate turnaround times while enhancing the diagnostic precision of radiologists.
 
 #### [MedImageParse](./deploy-medimageparse.md)
-This model is designed for precise image segmentation, and it covers various imaging modalities, including X-Rays, CT scans, MRIs, ultrasounds, dermatology images, and pathology slides. The model can be fine-tuned for specific applications, such as tumor segmentation or organ delineation, allowing developers to test and validate the model and the ability to build tools that leverage AI for highly sophisticated medical image analysis.
+This model is designed for precise image segmentation, and it covers various imaging modalities, including X-Rays, CT scans, MRIs, ultrasounds, dermatology images, and pathology slides. The model can be fine-tuned for specific applications, such as tumor segmentation or organ delineation. It allows developers to test and validate the model and the ability to build tools that leverage AI for highly sophisticated medical image analysis.
 
 ## Partner models
 
-The Azure AI model catalog also provides a curated collection of healthcare models from Microsoft partners with capabilities such as digital pathology slide analysis, biomedical research, and medical knowledge sharing. These models come from partners that include Paige.AI and Providence Healthcare. For a complete list of models, refer to the [model catalog page](https://aka.ms/healthcaremodelstudio). 
+The Azure AI model catalog also provides a curated collection of healthcare models from Microsoft partners with capabilities such as digital pathology slide analysis, biomedical research, medical knowledge sharing capabilities, and more. Partners including Paige.AI and Providence Healthcare provide these models. For a complete list of models, visit the [model catalog page](https://aka.ms/healthcaremodelstudio) resource.
 
 ## Related content
 

@@ -9,7 +9,7 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: tutorial
-ms.date: 12/27/2024
+ms.date: 02/13/2025
 ms.reviewer: tgokal
 ms.author: sgilley
 author: sdgilley