In 2024 we were excited to introduce the healthcare AI models in Azure AI Foundry model catalog. The collection included 18 open-source models for healthcare and life sciences spanning across first party, Microsoft Research, partner and third-party models from NVIDIA and Hugging Face. This launch represented a significant milestone, showcasing our commitment to providing accessible and advanced AI models to healthcare developers around the globe.

Since that pivotal moment, there have been substantial advancements. Our team has been diligently enhancing the Managed Compute offering in Foundry models, ensuring that we consistently expand our catalog for the benefit of our partners and customers.Now, we have more than 30 curated healthcare and life sciences  models in the catalog — and many more are in the pipeline.

Asset protection for AI models is now available

The current Foundry model catalog, enriched with open source models, stands as a testament to our dedication to democratizing AI for healthcare. These models encourage collaboration and innovation within the developer community.

Managed Compute now provides asset protection and a comprehensive monetization framework that brings significant benefits to model providers, driving both security and economic advantages. Enhanced security ensures that proprietary models are safeguarded against unauthorized access, maintaining the confidentiality of valuable assets and security. Conversely, open-source models provide benefits such as transparency, flexibility, and accelerated innovation.

Microsoft collaborates closely with partners such as Independent Software Vendors (ISVs), Academic Medical Centers (AMC), providers, payors and pharma companies to build high-quality, sustainable solutions including development and publishing AI models.

Understanding Asset Protection for Models

Asset protection remains a paramount concern for model providers who offer unique and proprietary AI solutions. To address this, we have implemented a deployment template feature, preventing unauthorized access to model assets, such as model weights and inference runtimes.

Asset protection is ensured on two fronts:

1. Model assets and runtime containers are not directly accessible by end users.
   • Model Metadata and the actual data are separated. Public storage container registries will allow users to read metadata of models, deployment templates, and environments (with container information), but actual data such as model weights and containers are behind the network boundary and protected with separate permission control.
   • Access to protected registries in production tenant is strictly governed and controlled.
2. End users cannot tweak the behavior of the container that serves the models.
   • Deployment template is authored and validated by model providers. Only validated deployment templates are allowed for deployment.
   • Closed deployment process ensures that the container serving the model is not tweaked by end users and only behave as intended/validated. If an end user tries to use an arbitrary container to serve the model, the request is rejected because the model is enforced to use the approved deployment template only. This method keeps model assets proprietary while enabling users to deploy models within their subscription.

Expanding models into pharma and life sciences

AI's importance in drug discovery cannot be overstated, as it revolutionizes the way researchers tackle complex biological challenges. The Microsoft Research (MSR) team has been actively developing innovative protein models, which are now being integrated into the Azure AI Foundry catalog.

• EvoDiff: A diffusion based generative model of protein sequences that can be used to design novel proteins with desirable properties otherwise inaccessible to structure-based models.
• BioEmu: The first model that generates structural ensembles with experimental accuracy capturing the dynamic flexibility of proteins that underpins protein function and revealing insights that static models miss.

Current research stages showcase AI's capability to accelerate protein folding, sequence generation, and molecular design, significantly reducing the time and cost associated with drug development. AI-driven models, such as NVIDIA's BioNeMo blueprint, exemplify the power of machine learning in predicting high-stability proteins and identifying effective drug targets.

These models are available in  Foundry Models catalog for Biology customers now, with additional models from their Generative Virtual Screening blueprint coming soon.

• ProteinMPNN generates and optimizes protein sequences, predicting high-stability, functional proteins. It supports innovation in protein-based drug development and synthetic biology.
• OpenFold2 predicts three-dimensional protein structures from amino acid sequences. It helps identify drug targets and design effective pharmaceuticals.
• RfDiffusion simulates molecular diffusion across cellular environments, offering insights into transport mechanisms and interactions. It's essential for studying signal transduction, metabolic pathways, and drug delivery systems, aiding in therapeutic strategy development.
• MSA-search (Multi Sequence Alignment) aligns multiple protein sequences to identify similarities and differences, crucial for comparative genomics and evolutionary biology. It helps understand evolutionary relationships and functional conservation, advancing genetic research and evolutionary studies.

Pathology Foundation models to improve diagnostic accuracy

Paige.ai in partnership with Microsoft Research has developed state-of-the-art digital pathology foundation models over the years.

• Virchow : The first million-slide-level foundation model proven to boost diagnostic accuracy across pan-cancer pathology applications.
• Virchow2 : Builds on Virchow with enhanced performance, striking the optimal balance between computational efficiency and diagnostic precision.
• Virchow2G : A large-scale Virchow-2 variant optimized for maximum downstream application accuracy.
• Virchow2G-mini : A compact Virchow-2 variant tuned for high throughput with minimal compromise to diagnostic performance.
• Prism : One of the first multi-modal, slide-level foundation models in digital pathology—excelling in both diagnostic classification and biomarker prediction.

While bringing models in the catalog is a significant achievement, the true potential is unlocked by connecting multiple models with agents using the Model Context Protocol (MCP) and Model Agents. This integration allows for seamless collaboration between diverse models, enhancing their collective capabilities and providing more comprehensive insights. By leveraging MCP and Model Agents, users can maximize the functionality of each model, leading to more accurate predictions, improved diagnostics, and optimized therapeutic strategies.  Starting MSBuild’25, we begin our agentic journey with a recently launched healthcare agent orchestrator in Azure AI Foundry.