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👀 Missed Session 02? Don’t worry—you can still catch up. But first, here’s what AI HLS Ignited is all about:

What is AI HLS Ignited?
AI HLS Ignited is a Microsoft-led technical series for healthcare innovators, solution architects, and AI engineers. Each session brings to life real-world AI solutions that are reshaping the Healthcare and Life Sciences (HLS) industry. Through live demos, architectural deep dives, and GitHub-hosted code, we equip you with the tools and knowledge to build with confidence.

Session 02 Recap:

In this session, we introduced MedEvals, an end-to-end evaluation framework for medical AI applications built on Azure AI Foundry. Inspired by Stanford’s MedHELM benchmark, MedEvals enables providers and payers to systematically validate performance, safety, and compliance of AI solutions across clinical decision support, documentation, patient communication, and more.

🧠 Why Scalable Evaluation Is Critical for Medical AI

“Large language models (LLMs) hold promise for tasks ranging from clinical decision support to patient education. However, evaluating the performance of LLMs in medical contexts presents unique challenges due to the complex and critical nature of medical information.”
— Evaluating large language models in medical applications: a survey

As AI systems become deeply embedded in healthcare workflows, the need for rigorous evaluation frameworks intensifies. Although large language models (LLMs) can augment tasks ranging from clinical documentation to decision support, their deployment in patient-facing settings demands systematic validation to guarantee safety, fidelity, and robustness. Benchmarks such as MedHELM address this requirement by subjecting models to a comprehensive battery of clinically derived tasks built on dataset (ground truth), enabling fine-grained, multi-metric performance assessment across the full spectrum of clinical use cases.​

However, shipping a medical LLM is only step one. Without a repeatable, metrics-driven evaluation loop, quality erodes, regulatory gaps widen, and patient safety is put at risk. This project accelerates your ability to operationalize trustworthy LLMs by delivering plug-and-play medical benchmarks, configurable evaluators, and CI/CD templates—so every model update triggers an automated, domain-specific “health check” that flags drift, surfaces bias, and validates clinical accuracy before it ever reaches production.

🚀 How to Get Started with MedEvals

Kick off your MedEvals journey by following our curated labs. Newcomers to Azure AI Foundry can start with the foundational workflow; seasoned practitioners can dive into advanced evaluation pipelines and CI/CD integration.

🧪 Labs

1. 🧪 Foundry Basics & Custom Evaluations: 🧾 Notebook
   
   
   
   • Authenticate, initialize a Foundry project, run built-in metrics, and build custom evaluators with EvalAI and PromptEval.
   
   
   
   


2. 🧪 Search & Retrieval Evaluations: 🧾 Notebook
   
   
   
   • Prepare datasets, execute search metrics (precision, recall, NDCG), visualize results, and register evaluators in Foundry.
   
   
   
   


3. 🧪 Repeatable Evaluations & CI/CD: 🧾 Notebook
   
   
   
   • Define evaluation schemas, build deterministic pipelines with PyTest, and automate drift detection using GitHub Actions.
   
   
   
   

🏥 Use Cases

📝 Creating Your Clinical Evaluation with RevCycle Determinations

Select a model and metric that best supports the determination behind the rationale made on AI-assisted prior authorizations based on real payor policy.

This notebook use case includes:

• Selecting multiple candidate LLMs (e.g., gpt-4o, o1)


• Breaking down determinations both in deterministic results (approved vs rejected) and the supporting rationale and logic.


• Running evaluations across multiple dimensions


• Combining deterministic evaluators and LLM-as-a-Judge methods


• Evaluating the differential impacts of evaluators on the rationale across scenarios

🧾Get Started with the Notebook

Why it matters:
Enables data-driven metric selection for clinical workflows, ensures transparent benchmarking, and accelerates safe AI adoption in healthcare.

📝 Evaluating AI Medical Notes Summarization Applications

Systematically assess how different foundation models and prompting strategies perform on clinical summarization tasks, following the MedHELM framework.
This notebook use case includes:

• Preparing real-world datasets of clinical notes and summaries


• Benchmarking summarization quality using relevance, coherence, factuality, and harmfulness metrics


• Testing prompting techniques (zero-shot, few-shot, chain-of-thought prompting)


• Evaluating outputs using both automated metrics and human-in-the-loop scoring

🧾Get Started with the Notebook

Why it matters:
Ensures responsible deployment of AI applications for clinical summarization, guaranteeing high standards of quality, trustworthiness, and usability.

📣 Join Us for the Next Session

Help shape the future of healthcare by sharing AI HLS Ignited with your network—and don’t miss what’s coming next!

📅 Register for the upcoming session → AI HLS Ignited Event Page
💻 Explore the code, demos, and architecture → AI HLS Ignited GitHub Repository