changing order of paragraphs to open with learning objectives

Author: Ivan Tarapov

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

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 [!INCLUDE [health-ai-models-meddev-disclaimer](../../includes/health-ai-models-meddev-disclaimer.md)]
 
-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.
+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.
 
-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 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":::
 
-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.
+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. 
 
 ## Microsoft first-party models