Microsoft Introduces MedEvals on Azure AI Foundry for Comprehensive Clinical AI Validation in Healthcare

Microsoft’s AI HLS Ignited series introduces MedEvals, a robust evaluation framework on Azure AI Foundry designed to ensure the safety, accuracy, and compliance of large language models (LLMs) in healthcare. MedEvals automates clinical AI validation, accelerating trustworthy AI adoption in medical workflows. Unique :

Monitoring and Evaluating Medical AI with Azure AI Foundry

Microsoft’s AI HLS Ignited series is empowering healthcare innovators with cutting-edge AI tools. The latest session introduced MedEvals, a robust evaluation framework for medical AI applications built on Azure AI Foundry.

What’s New: Meet MedEvals

MedEvals is inspired by Stanford’s MedHELM benchmark and designed to validate AI models in clinical settings. It helps healthcare providers and payers systematically assess AI solutions for safety, compliance, and performance.

“Large language models (LLMs) hold promise for tasks ranging from clinical decision support to patient education.” – Evaluating large language models in medical applications: a survey

This framework covers a broad spectrum of clinical use cases, including decision support, documentation, and patient communication. It’s built to handle the complexity and critical nature of medical data, ensuring AI models meet rigorous standards before deployment.

Major Updates: Why Scalable Evaluation Matters

Deploying LLMs in healthcare requires more than just initial testing. Without continuous, metrics-driven evaluation, model quality can degrade, and patient safety risks increase.

MedEvals accelerates trustworthy AI deployment by offering plug-and-play benchmarks, configurable evaluators, and CI/CD templates. Every model update triggers an automated “health check” that detects bias, drift, and clinical inaccuracies.

“Without a repeatable, metrics-driven evaluation loop, quality erodes, regulatory gaps widen, and patient safety is put at risk.”

Getting Started: Hands-On Labs and Use Cases

Microsoft provides curated labs to help users explore MedEvals, whether they’re new to Azure AI Foundry or seasoned AI engineers. Labs cover foundational workflows, search and retrieval metrics, and automated CI/CD pipelines for evaluation.

Foundry Basics & Custom Evaluations: Authenticate, initialize projects, and build custom evaluators.
Search & Retrieval Evaluations: Prepare datasets, run precision and recall metrics, and visualize results.
Repeatable Evaluations & CI/CD: Automate drift detection and build deterministic pipelines using GitHub Actions.

Two standout use cases include:

Clinical Evaluation for Prior Authorizations

This notebook helps select models and metrics to support AI-assisted prior authorizations based on payer policies. It evaluates multiple LLM candidates and analyzes rationale behind determinations.

Evaluating AI Medical Notes Summarization

Following the MedHELM framework, this use case benchmarks clinical summarization models on relevance, coherence, factuality, and harmfulness, using both automated and human-in-the-loop scoring.

Why It Matters for Healthcare AI

MedEvals ensures AI models in healthcare are transparent, reliable, and safe. By enabling data-driven metric selection and continuous monitoring, it accelerates responsible AI adoption in clinical workflows.

For tech innovators in healthcare, this means building AI solutions with confidence and meeting stringent regulatory demands.

Join the AI HLS Ignited Community

Don’t miss upcoming sessions to stay ahead in healthcare AI innovation. Explore the code, demos, and architecture on the AI HLS Ignited GitHub Repository.

Ready to dive in? Check out the labs and start evaluating medical AI models with Azure AI Foundry today!

MedEvals is inspired by Stanford’s MedHELM benchmark, providing clinically derived tasks for comprehensive LLM evaluation.

Automated CI/CD pipelines enable continuous monitoring of AI model drift, bias, and clinical accuracy before deployment.

Hands-on notebooks guide users through foundational to advanced evaluation workflows, including search metrics and custom evaluators.

Use cases cover critical healthcare applications such as prior authorization determinations and clinical note summarization.

MedEvals supports multi-metric assessments including relevance, coherence, factuality, and harmfulness to ensure responsible AI use.

From the New blog articles in Microsoft Community Hub