Monitoring and Evaluating LLMs in Clinical Contexts with Azure AI Foundry
👀 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 🧪 Foundry Basics & Custom Evaluations: 🧾 Notebook Authenticate, initialize a Foundry project, run built-in metrics, and build custom evaluators with EvalAI and PromptEval. 🧪 Search & Retrieval Evaluations: 🧾 Notebook Prepare datasets, execute search metrics (precision, recall, NDCG), visualize results, and register evaluators in Foundry. 🧪 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
Orchestrate multimodal AI insights within your healthcare data estate (Public Preview)
In today’s healthcare landscape, there is an increasing emphasis on leveraging artificial intelligence (AI) to extract meaningful insights from diverse datasets to improve patient care and drive clinical research. However, incorporating AI into your healthcare data estate often brings significant costs and challenges, especially when dealing with siloed and unstructured data.​ Healthcare organizations produce and consume data that is not only vast but also varied in format—ranging from structured EHR entries to unstructured clinical notes and imaging data. Traditional methods require manual effort to prepare and harmonize this data for AI, specify the AI output format, set up API calls, store the AI outputs, integrate the AI outputs, and analyze the AI outputs for each AI model or service you decide to use. Orchestrate multimodal AI insights is designed to streamline and scale healthcare AI within your data estate by building off of the data transformations in healthcare data solutions in Microsoft Fabric. This capability provides a framework to generate AI insights by connecting your multimodal healthcare data to an ecosystem of AI services and models and integrating structured AI-generated insights back into your data estate. When you combine these AI-generated insights with the existing healthcare data in your data estate, you can power advanced analytics scenarios for your organization and patient population. Key features: Metadata store lakehouse acts as a central repository for the metadata for AI orchestration to effectively capture and manage enrichment definitions, view definitions, and contextual information for traceability purposes. Execution notebooks define the enrichment view and enrichment definition based on the model configuration and input mappings. They also specify the model processor and transformer. The model processor calls the model API, and the transformer produces the standardized output while saving the output in the bronze lakehouse in the Ingest folder. Transformation pipeline to ingest AI-generated insights through the healthcare data solutions medallion lakehouse layers and persist the insights in an enrichment store within the silver layer. Conceptual architecture: The data transformations in healthcare data solutions in Microsoft Fabric allow you ingest, store, and analyze multimodal data. With the orchestrate multimodal AI insights capability, this standardized data serves as the input for healthcare AI models. The model results are stored in a standardized format and provide new insights from your data. The diagram below shows the flow of integrating AI generated insights into the data estate, starting as raw data in the bronze lakehouse and being transformed to delta tables in the silver lakehouse. This capability simplifies AI integration across modalities for data-driven research and care, currently supporting: Text Analytics for health in Azure AI Language to extract medical entities such as conditions and medications from unstructured clinical notes. This utilizes the data in the DocumentReference FHIR resource. MedImageInsight healthcare AI model in Azure AI Foundry to generate medical image embeddings from imaging data. This model leverages the data in the ImagingStudy FHIR resource. MedImageParse healthcare AI model in Azure AI Foundry to enable segmentation, detection, and recognition from imaging data across numerous object types and imaging modalities. This model uses the data in the ImagingStudy FHIR resource. By using orchestrate multimodal AI insights to leverage the data in healthcare data solutions for these models and integrate the results into the data estate, you can analyze your existing data alongside AI enrichments. This allows you to explore use cases such as creating image segmentations and combining with your existing imaging metadata and clinical data to enable quick insights and disease progression trends for clinical research at the patient level. Get started today! This capability is now available in public preview, and you can use the in-product sample data to test this feature with any of the three models listed above. For more information and to learn how to deploy the capability, please refer to the product documentation. We will dive deeper into more detailed aspects of the capability, such as the enrichment store and custom AI use cases, in upcoming blogs. 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. FHIR® is the registered trademark of HL7 and is used with permission of HL7.
