Merge pull request #996 from ivantarapov/itarapov/healthcare-ai-models-posthlth-fixes

A set of fixes post HLTH24 show

Author: prmerger-automator[bot]

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

@@ -27,7 +27,7 @@ In this article, you learn how to deploy MedImageInsight from the model catalog
 * Send test data to the model, receive, and interpret results
 
 ## MedImageInsight - the medical imaging embedding model
-MedImageInsight foundational model for health is a powerful model that can process a wide variety of medical images. These images include X-Ray, CT, MRI, clinical photography, dermoscopy, histopathology, ultrasound, and mammography images. Rigorous evaluations demonstrate MedImageInsight's ability to achieve state-of-the-art (SOTA) or human expert-level performance across classification, image-to-image search, and fine-tuning tasks. Specifically, on public datasets, MedImageInsight achieves or exceeds SOTA performance in chest X-ray disease classification and search, dermatology classification and search, Optical coherence tomography (OCT) classification and search, and 3D medical image retrieval. The model also achieves near-SOTA performance for histopathology classification and search.  
+MedImageInsight foundation model for health is a powerful model that can process a wide variety of medical images. These images include X-Ray, CT, MRI, clinical photography, dermoscopy, histopathology, ultrasound, and mammography images. Rigorous evaluations demonstrate MedImageInsight's ability to achieve state-of-the-art (SOTA) or human expert-level performance across classification, image-to-image search, and fine-tuning tasks. Specifically, on public datasets, MedImageInsight achieves or exceeds SOTA performance in chest X-ray disease classification and search, dermatology classification and search, Optical coherence tomography (OCT) classification and search, and 3D medical image retrieval. The model also achieves near-SOTA performance for histopathology classification and search.  
 
 An embedding model is capable of serving as the basis of many different solutions—from classification to more complex scenarios like group matching or outlier detection. The following animation shows an embedding model being used for image similarity search and to detect images that are outliers.
 

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 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
@@ -14,34 +14,34 @@ 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
 ---
 
-# Foundational AI models for healthcare
+# Foundation models for healthcare AI
 
 [!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 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.
 
 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 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)]
 
 ## 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 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 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.  
+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.  
 
 #### [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. This capability accelerates turnaround times while enhancing the diagnostic precision of radiologists. 
+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. 
 
 #### [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 build tools on top of this model 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, allowing developers to test and validate the model and the ability to build tools that leverage AI for highly sophisticated medical image analysis.
 
 ## Partner models
 

articles/ai-studio/includes/health-ai-models-meddev-disclaimer.md

@@ -11,4 +11,4 @@ ms.custom: include
 ---
 
 > [!IMPORTANT]
-> Microsoft provides these models on an "as is" basis.  Microsoft makes no warranties, express or implied, guarantees, or conditions with respect to your use of the models. To the extent permitted under your local law, Microsoft disclaims all liability for any damages or losses, including direct, consequential, special, indirect, incidental, or punitive, resulting from your use of the models. Microsoft products and services (1) are not designed, intended, or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or substitute for professional medical advice, diagnosis, treatment, or judgment.  You are responsible for ensuring solutions comply with applicable laws and regulations.
+> The healthcare AI models are intended for research and model development exploration. The models are not designed or intended to be deployed in clinical settings as-is nor for use in the diagnosis or treatment of any health or medical condition, and the individual models’ performances for such purposes have not been established. You bear sole responsibility and liability for any use of the healthcare AI models, including verification of outputs and incorporation into any product or service intended for a medical purpose or to inform clinical decision-making, compliance with applicable healthcare laws and regulations, and obtaining any necessary clearances or approvals.