In today's rapidly evolving healthcare landscape, AI is revolutionizing patient care by enabling more personalized experiences, optimizing vast medical data management, and improving patient outcomes. As challenges such as rising patient expectations, complex data processing and intensifying regulatory requirements, more sophisticated solutions have become essential.
Microsoft is at the forefront of this transformation dedicated to developing and implementing responsible AI technologies. By fostering innovation and collaboration through Microsoft Cloud for Healthcare, we continue to enhance the way AI enhances healthcare delivery and improves outcomes for patients around the world. Based on this commitment, we are excited to introduce new features to our AI Healthcare portfolio.
Integrating advanced AI models in healthcare
As medical technology advances, improving medical imaging is important for better diagnosis of disease and improving patient care. In 2024, we announced the launch of Healthcare AI Models, a collection of cutting-edge multimodal medical imaging foundation models available in Azure AI Foundry. Designed for accurate image segmentation, MedimageParse 2D models cover many image modalities including X-rays, CTS, MRI, ultrasound, dermatological images, and pathological slides. It can be fine-tuned for specific applications such as tumor segmentation and organ depiction, allowing developers to test and verify their ability to leverage AI for highly targeted cancer and other disease detection, diagnosis and treatment planning.
Today we are excited that the MedimageParse model is optimized for 3D medical imaging data. MedimageParse 3D can process complex 3D datasets produced by advanced imaging such as MRI and CT scans, providing a more comprehensive view of the patient's condition. Enhanced ability to visualize and interpret anatomical abnormalities and structures provides a more accurate diagnosis that may have been overlooked in 2D analysis. MedimageParse can support healthcare researchers with comprehensive image analysis and a more streamlined workflow for radiologists, improving overall efficiency and reducing human error. MedimageParse 3D will soon be found in the Azure AI Foundry model catalog.
The Microsoft Health and Life Sciences Model Catalog also features several new updated multimodal medical foundation models, including Tamgen for protein design, Hist-ai in pathology, and ECG-FM for Eletherocardiogram (ECG) analysis, in partnership with the Microsoft Health and Life Sciences Model Catalog.
Use multimodal AI to improve health insights
Today we are pleased to announce new features in our healthcare data solutions that allow our customers to tailor multimodal AI insights directly to Microsoft fabrics. The currently published preview allows healthcare organizations to generate robust insights that help to make faster decisions and improve patient outcomes by adjusting multiple modalities of health data within the fabric (text, images, audio, video, and other forms of sensory input).
Customers can leverage fabrics to tailor multimodal AI insights by connecting their healthcare data to a variety of AI services and models. These AI-generated insights are then integrated into the Healthcare Data Estate to enable a variety of use cases, including targeted outreach and care plans, by enriching clinical conversations between social determinants of health (SDOH) and emotions. Another possible scenario is to create image segmentation and derive rapid insights in clinical research and trends in disease progression by combining it with imaging metadata via Microsoft Power BI reports.
The orchestration feature includes five ready-to-use examples to help customers connect and integrate with AI models.
Azure AI Language Health Text Analysis extracts medical entities from unstructured data such as diagnosis and drugs, and relationships between entities. Azure AI Foundry's MedimageInsight AI model generates embeddings of medical images from imaging data. Azure AI Foundry's MedimageParse AI model allows segmentation, detection, and recognition from image data across many object types and imaging modalities. Sentiment analysis with Azure Openai services acquires emotions in categories such as doctor services, staff services, facilities, and costs from conversation data. SDOH extraction with Azure OpenaI extracts social determinants of health data from conversational data based on centers in defined categories of Medicare and Medicare services.
To further enhance data accessibility, we are pleased to share the general availability of additional features that enhance existing capabilities within the healthcare data solution offerings. These include:
Care Management Analysis: Using unified healthcare data and care management analysis templates, providers can enhance patient care by identifying high-risk individuals, optimizing treatment plans, and improving care coordination. This allows organizations to provide personalized, efficient and proactive care. Patient Outreach Analysis: Healthcare providers communicate with patients more effectively by coordinating personalized journeys beyond patient touchpoints. This feature simplifies the process by bringing data from a variety of sources into the fabric, converting it into an industry data model, and providing it to Power BI reports. Dragon Copilot Ambient AI Integration: Dragon Copilot's AI-powered voice-enabled features reduce the administrative workload of clinicians by automatically documenting patient encounters. With fabric integration, this new feature brings conversational data to Fabric Onelake. This integration allows customers to access, store and manage raw data generated. Data is stored in lake houses and organized into a hierarchical structure by date, allowing customers to view each file and its content. When used in conjunction with Healthcare Data Solutions, customers can combine conversational and clinical data to learn more from patient interactions.
“There are many unrealized value in patient physician interactions. OSUMC aims to leverage conversational data along with multimodal AI insights in healthcare data solutions, such as social determinants of health extraction, to improve patient outcomes.”
– Rabbi Dita, director of Ohio State University Wexner Medical Center
Achieve more with reliable AI
This week's announcement of Microsoft Cloud for Healthcare highlights our commitment to transforming healthcare through advanced AI models and data integration. By leveraging these cutting-edge technologies, we empower healthcare organizations to deliver better care, improve patient outcomes, and drive industry innovation.
Connect with us with Microsoft Booth #2221 at HIMSS 2025 and immerse yourself in the latest advances in data and AI from Microsoft and Partners.
Microsoft Cloud for Healthcare
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Medical Device Disclaimer: Microsoft products and services (1) are not designed, intended or made available as medical devices, and (2) should not be designed or intended in place of professional medical advice, diagnosis, treatment, or judgment, or should not be used in place of professional medical advice, diagnosis, treatment, or judgment. The customer/partner is responsible for ensuring that the solution is in compliance with applicable laws and regulations.
Generated AI does not always provide accurate or complete information. The AI output does not reflect Microsoft's opinion. Customers/partners should thoroughly test and evaluate whether AI tools are suitable for use. Customers/partners should thoroughly review product documentation for each tool.
David Ardman is responsible for building first-party Microsoft Solutions and Healthcare and Life Sciences (HLS) data and AI platforms that enhance both healthcare clients and partners through third-party cloud services.
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