foundational-foundation rename

Author: Ivan Tarapov

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

@@ -1,5 +1,5 @@
 ---
-title: Foundational AI models for healthcare in AI Studio
+title: Foundation AI models for healthcare in AI Studio
 titleSuffix: Azure AI Studio
 description: Learn about AI models that are applicable to the health and life science industry.
 ms.service: azure-ai-studio
@@ -14,25 +14,25 @@ author: msakande
 #Customer intent: As a Data Scientist I want to learn what offerings are available within Health and Life Sciences AI Model offerings so that I can use them as the basis for my own AI solutions
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-# Foundational AI models for healthcare
+# foundation AI models for healthcare
 
 [!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
 
 [!INCLUDE [health-ai-models-meddev-disclaimer](../../includes/health-ai-models-meddev-disclaimer.md)]
 
-In this article, you learn about Microsoft's catalog of foundational multimodal healthcare AI 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 Microsoft's catalog of foundation multimodal healthcare AI 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.
 
 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.
 
 :::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 foundational healthcare AI 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](../model-catalog-overview.md) provides foundation healthcare AI 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)]
 
 ## Microsoft first-party models
 
-The following models are Microsoft's first party foundational multimodal healthcare AI models.
+The following models are Microsoft's first party foundation multimodal healthcare AI 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 directly or build adapters for their specific tasks, thereby streamlining workflows in radiology, pathology, ophthalmology, dermatology, and other modalities. For example, 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 improve efficiency and patient outcomes. Furthermore, the model can be used 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.