Leading the charge to transform healthcare with advanced AI
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 handling, and regulatory requirements intensify, more advanced 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 reinforce how responsible AI can enhance healthcare delivery and improve outcomes for patients worldwide. Building on this commitment, we’re excited to introduce new features in our AI healthcare portfolio that will further drive industry efficiencies, and better patient outcomes.
Advanced AI models and integrations for healthcare
As medical technology advances, improvements in medical imaging are critical for better diagnosis of disease and improved 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 precise image segmentation, MedImageParse 2D model covers many imaging modalities, including x-rays, CTs, MRIs, ultrasounds, dermatology images, and pathology slides. It can be fine-tuned for specific applications such as tumor segmentation or organ delineation, allowing developers to test and validate the ability to leverage AI for highly targeted cancer and other disease detection, diagnostics, and treatment planning.
Today, we’re excited to share the MedImageParse model is now optimized for 3D medical imaging data. MedImageParse 3D can handle complex 3D datasets produced by advanced imaging, such as MRI and CT scans, providing a more comprehensive view into patients’ conditions. The enhanced ability to visualize and interpret anatomical abnormalities and structures provides for much more accurate diagnosis that may have been missed by 2D analysis. MedImageParse can also support healthcare researchers with comprehensive image analysis and a more streamlined workflow for radiologists, improving overall efficiency and reducing human error. MedImageParse 3D can soon be found in the Azure AI Foundry model catalog.
In partnership with Microsoft Research, the Microsoft Health and Life Sciences model catalog will also feature several new and updated multimodal medical foundation models including TamGen for protein design, Hist-ai for pathology, and ECG-FM for electrocardiogram (ECG) analysis.
Leveraging multimodal AI for improved health insights
Today, we are excited to announce new functionality in healthcare data solutions that allows customers to orchestrate multimodal AI insights directly into Microsoft Fabric. Now in public preview, orchestrating multiple modalities (e.g., text, image, audio, video, and other forms of sensory input) of health data within Fabric allows healthcare organizations to generate a robust set of insights that help faster decision-making and improved patient outcomes.
Customers can leverage Fabric to orchestrate multimodal AI insights by connecting their healthcare data to a variety of AI services and models. These AI-generated insights are then integrated back into the healthcare data estate to enable various use cases like creating targeted outreach and care plans by enriching clinical conversations with social determinants of health (SDOH) and sentiments. Another possible scenario is deriving quick insights and disease progression trends for clinical research by creating image segmentations and combining it with imaging metadata through Microsoft Power BI reports.
The orchestration capability includes five out-of-the-box examples to help customers connect and integrate to AI models:
- Text analytics for health in Azure AI Language to extract medical entities from unstructured data such as diagnoses and medications, and the relations between entities.
- MedImageInsight AI model in Azure AI Foundry to generate medical image embeddings from imaging data.
- MedImageParse AI model in Azure AI Foundry enables segmentation, detection, and recognition from imaging data across numerous object types and imaging modalities.
- Sentiment analysis with Azure OpenAI Service to score sentiment for categories such as doctors’ services, staff services, facilities, and cost from conversational data.
- SDOH extraction with Azure OpenAI to extract social determinants of health data from conversational data based on the Centers for Medicare and Medicaid Services’ defined categories.
To further enhance data accessibility, we’re pleased to share the general availability of additional functionality that enhances the existing capabilities within our healthcare data solutions offering. These include:
- Centers for Medicare and Medicaid Services Claim and Claim Line Feed Files (CMS CCLF) claims data ingestion: Customers can bring CMS CCLF claims data into Fabric where they can assess the effectiveness of their care management programs, monitor population-level trends and usage as well as measure their performance against benchmarks to reduce overall claim expenses and improve patient care.
- Social determinants of health public datasets transformation: Now healthcare organizations can ingest, persist, harmonize, and use SDOH public datasets to identify risks and health-related social needs, ultimately creating more equitable healthcare for all patients and communities.
- Care management analytics: By using unified healthcare data and care management analytical templates, healthcare providers can enhance patient care by identifying high-risk individuals, optimizing treatment plans, and improving care coordination. This empowers organizations to deliver personalized, efficient, and proactive care.
- Patient outreach analytics: Healthcare providers communicate with their patients more effectively by orchestrating personalized journeys across patient touchpoints. This capability simplifies the process by bringing data from different sources into Fabric, transforming it into an industry data model, and serving it to a Power BI report.
- Dragon Copilot ambient AI integration: Dragon Copilot’s AI-powered, voice-enabled capabilities reduce the administrative workload of clinicians by automatically documenting patient encounters. With integration into Fabric, this new capability brings conversational data into Fabric OneLake. This integration enables customers to access, store, and manage the raw data generated. The data is stored in a lakehouse, organized in a hierarchical structure by date, which lets customers view each file and its content. When used in conjunction with healthcare data solutions, customers can combine their conversational data with their clinical data to learn more from patient interactions.
“There is a lot of unrealized value in patient physician interactions. OSUMC is aiming to leverage conversational data along with multimodal AI insights in healthcare data solutions such as social determinants of health extraction to improve patient outcomes.”
—Ravi Dyta, Director of IT at Ohio State University Wexner Medical Center
Achieve more with AI you can trust
This week’s Microsoft Cloud for Healthcare announcements underscore our commitment to transforming healthcare through advanced AI models and data integrations. By leveraging these cutting-edge technologies, we’re empowering healthcare organizations to deliver better care, help improve patient outcomes, and drive innovation in the industry.
Connect with us in the Microsoft booth #2221 at HIMSS 2025 to immerse yourself in the latest advancements in data and AI from Microsoft and our partners.
Medical device disclaimer: 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 as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations.
Generative AI does not always provide accurate or complete information. AI outputs do not reflect the opinions of Microsoft. Customers/partners will need to thoroughly test and evaluate whether an AI tool is fit for the intended use and identify and mitigate any risks to end users associated with its use. Customers/partners should thoroughly review the product documentation for each tool.
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Source: Microsoft Industry Blog