Introducing healthcare AI model evaluator: an open-source framework for healthcare AI evaluation

From AI orchestration to AI trust

At Microsoft Build, we unveiled the healthcare agent orchestrator  —a modular, open-source framework that enables healthcare organizations to compose, coordinate, and govern AI agents across clinical workflows. This orchestration layer represents a significant advancement, moving beyond isolated model endpoints to intelligent, auditable workflows that reflect the complexities of clinical practice.

The healthcare agent orchestrator empowers developers and clinicians to:

• Chain multiple AI models and tools to complete complex clinical tasks, including patient history summarization, discharge instruction generation, and message triage
• Integrate with electronic health records (EHRs), imaging systems, and clinical databases
• Support safety protocols, compliance requirements, and human-in-the-loop review at every critical decision point

Building on this foundation, we are excited to introduce healthcare AI model evaluator, available now on GitHub—an open-source evaluation framework that enables healthcare organizations to rigorously benchmark AI systems using their own data, clinical tasks, and performance metrics.

While the healthcare agent orchestrator helps you build and deploy AI workflows, healthcare AI model evaluator helps you assess them for your needs.

The healthcare AI trust gap

 

Healthcare organizations face a critical challenge in AI adoption. Despite the transformative potential of AI for patient care, administrative efficiency, and clinical outcomes, most healthcare leaders find themselves navigating between vendor promises and real-world uncertainty. Generic leaderboards can display impressive accuracy scores, but these metrics often fail to address fundamental questions:

• Will these models perform reliably with our specific patient population?
• Do they integrate effectively with our clinical workflows?
• Can they handle our unique use cases and edge cases?

This trust gap represents a primary barrier to widespread healthcare AI adoption—not a lack of capable models, but a lack of transparent, context-specific evaluation frameworks.

The healthcare AI evaluation challenge

There are several persistent considerations that can cause healthcare organizations to hesitate to deploy AI systems:

Lack of contextual relevance

• Generic benchmarks that fail to reflect specific clinical contexts and patient demographics
• Limited visibility into model performance on rare conditions or minority subgroups
• Insufficient evidence of cross-institutional generalizability

Evaluation complexity

• Deep data science expertise required for rigorous model evaluation
• Complex multimodal data integration across EHRs, imaging systems, and laboratory systems
• Continuous monitoring requirements for model drift and dataset shifts

Transparency and trust deficits

• Opaque or difficult to reproduce evaluation methodologies from AI vendors
• Limited ability to test models on organization-specific use cases
• Insufficient evidence of real-world clinical, administrative, or operational impact

Operational realities

• Inter-rater variability and label noise among clinical experts
• Missing or asynchronous data from disparate healthcare systems
• Complex patient presentations involving multiple comorbidities and medication regimens
• Alert fatigue, automation bias, and other clinician-AI interaction effects

Current vendor-provided leaderboards offer only superficial insights into model accuracy. Healthcare organizations require the capability to evaluate AI systems using their own data, success criteria, and clinical expertise—a capability that has remained largely inaccessible until now.

Introducing Healthcare AI model evaluator: rigorous evaluation on your terms

Healthcare AI model evaluator addresses these challenges with a comprehensive, open-source evaluation framework that puts healthcare organizations in complete control of the AI assessment process.

Foundational principles

Healthcare AI model evaluator was architected from the ground up with healthcare requirements at its core:

Data sovereignty Deploy healthcare AI model evaluator within your own secure infrastructure, helping to ensure sensitive data stays within your control.

Your data, your context Evaluate models using datasets that authentically reflect your patient populations, clinical scenarios, and organizational priorities—not synthetic benchmarks that may obscure real-world performance gaps.

Clinical task alignment Define evaluation tasks that directly address your organization's clinical priorities, from diagnostic decision support to administrative automation and care coordination.

Expert-driven assessment Leverage the clinical and operational expertise within your organization to establish evaluation criteria, interpret results, and validate AI performance against professional standards.

Custom success metrics Measure what matters to your organization—whether clinical accuracy, workflow efficiency, patient safety, algorithmic fairness, or cost-effectiveness.

Model agnosticism Evaluate any AI system: commercial API endpoints, open-source models, proprietary custom solutions, or ensemble approaches.

Key capabilities

No-code evaluation workflow

Healthcare AI model evaluator features an intuitive web interface designed for clinical staff with no programming expertise. Healthcare professionals can configure evaluations, review model outputs, and provide expert feedback through a streamlined, user-friendly platform. This approach ensures that human evaluation—essential for trustworthy AI in healthcare—is accessible to all, regardless of technical background. By lowering the barrier to participation, healthcare AI model evaluator enables organizations to make use of the full value of their clinical expertise in AI assessment.

Flexible dataset management

Complete data control

Healthcare AI model evaluator ensures complete control over evaluation data by maintaining all information securely within your organization's perimeter. The platform supports both structured and unstructured clinical text—including notes, summaries, and recommendations—allowing for flexible data management. Its multimodal evaluation capabilities enable the assessment of medical imaging alongside corresponding clinical documentation, fostering comprehensive analysis. Additionally, automated output generation streamlines comparative model analysis, facilitating efficient and thorough performance reviews.

Customizable clinical task framework

Task-specific evaluation design

The framework enables users to define custom clinical tasks by leveraging prompt engineering and specifying detailed evaluation criteria. Inputs, ground truth references, and model outputs are systematically mapped to facilitate comprehensive comparisons. Task-specific metrics can be configured to align precisely with clinical workflow requirements, helping to ensure relevance and accuracy in assessments. Additionally, users benefit from built-in templates tailored for common healthcare AI use cases that can streamline the setup of evaluations and enhance consistency across projects.

Human-in-the-loop evaluation

Healthcare AI model evaluator enables comprehensive hybrid quantitative and qualitative assessment by integrating automated metrics with expert clinical judgment. This approach is further strengthened by support for role-based access control and clinical expertise classification, helping to ensure that evaluations are performed by appropriately qualified individuals. Flexible evaluation workflows—such as binary judgments, Likert scales, or full text editing—allow for tailored human-in-the-loop assessment processes. Additionally, AI-assisted evaluation using model-as-a-judge methodologies augment the assessment, blending machine efficiency with human insight. The insights gathered from these expert evaluations can be invaluable, helping validate current AI performance, inform enhancements to clinical workflows, and guide the evolution of future AI models. Expert feedback supports model fine-tuning and reinforcement learning, helping ensure that AI systems continually adapt to real-world needs and professional standards.

Comprehensive model endpoint management

Universal model integration

Healthcare AI model evaluator provides universal model integration, allowing  connectivity to any AI model endpoint—including commercial APIs, open-source frameworks, or proprietary implementations. The platform supports virtual endpoints to facilitate the evaluation of pre-existing model outputs for flexibility in assessment workflows. Real-time cost tracking is available across various models and evaluation scenarios, empowering users with detailed financial oversight. Additionally, continuous performance monitoring and drift detection capabilities help ensure that models maintain reliability and accuracy throughout their lifecycle.

Analytics and reporting

Healthcare AI model evaluator aims to deliver actionable insights through multi-dimensional visualization tools so users can analyze performance at the task and model levels, review cost analyses, and track custom metrics. The platform supports comprehensive data export, facilitating downstream analysis and ensuring readiness for regulatory documentation. With native support for healthcare-specific AI evaluation metrics and an extensible framework for developing custom metrics and visualizations, healthcare AI model evaluator empowers organizations to tailor analytics to their unique clinical and operational needs.

Building on a strong foundation

Healthcare AI model evaluator acknowledges and complements recent advances in healthcare AI evaluation, including OpenAI's HealthBench, Stanford's MedHelm, Harvard/Mass General Brigham's BRIDGE project, and numerous other academic and industry initiatives. Our focus on organizational autonomy and clinical practitioner empowerment offers a complementary approach to these valuable efforts.

Healthcare AI model evaluator also extends Microsoft's existing healthcare AI capabilities, such as:

• Microsoft Foundry: Leveraging comprehensive evaluation tooling
• Azure Machine Learning: Building on healthcare-specific model templates
• Published research: Incorporating metrics from RadFact, TBFact, ACIBench, and related publications
• Clinical safeguards: Integrating with fully managed healthcare AI safety services

Vision: accelerating safe, equitable AI adoption

By democratizing access to careful AI evaluation, healthcare AI model evaluator aims to accelerate the adoption of effective, safe, and equitable AI systems across healthcare. When clinicians can evaluate AI tools using their own data and expertise, they develop the evidence-based trust necessary for successful clinical implementation.

Healthcare AI model evaluator enables healthcare organizations to move beyond vendor promises and generic leaderboards toward data-driven AI adoption decisions. It places the power of evaluation directly in the hands of those who understand clinical needs best—healthcare practitioners themselves.

The complete ecosystem: Microsoft Foundry HLS models + orchestration + evaluation

Microsoft's unified Healthcare AI ecosystem brings together purpose built-models, orchestration tools, and robust evaluation capabilities to deliver an integrated solution for advancing clinical AI:

Microsoft Foundry – healthcare AI models: Catalog of models purpose-built for healthcare and life sciences use cases

Healthcare agent orchestrator: Sample code with pre-configured agents with multi-agent orchestration and open-source customization options that aid in the creation of agents that coordinate complex workflows.

Healthcare AI model evaluator: Powerful tool for testing and validating AI model performance on relevant clinical tasks using their own data and in their own environment, usi

Fine-tuning with human feedback : Sample code with detailed documentation which can leverage the results from model evaluations and real-world customer data to further improve model accuracy by fine-tuning existing healthcare AI models. Incorporating human feedback helps adapt models to specific clinical contexts and evolving requirements.

By incorporating input from clinicians and other experts into every stage of the model's lifecycle, this approach continually enhances AI, making solutions more precise and relevant to clinical practice. As a result, healthcare organizations can move from experimenting with AI to using these solutions with confidence, benefiting from greater transparency and alignment to clinical needs.

Getting started with healthcare AI model evaluator

Healthcare AI model evaluator is available now as an open-source project. Healthcare organizations can:

1. Deploy locally: Maintain control over data and privacy within your infrastructure
2. Customize workflows: Adapt evaluation processes to match specific clinical requirements
3. Integrate seamlessly: Connect with existing AI endpoints and healthcare information systems
4. Scale strategically: Expand evaluation efforts across departments, specialties, and use cases

The platform is architected for ease of deployment, operational simplicity, methodological transparency, experimental reproducibility, functional extensibility, and continuous evaluation to enable ongoing assessment as models and clinical needs evolve.

Join the healthcare AI model evaluator community

Healthcare AI model evaluator represents a collaborative effort to address the healthcare AI trust gap through open, transparent, and clinically grounded evaluation methodologies.  We invite clinicians, data scientists, healthcare administrators, AI researchers, and patient advocates to join the healthcare AI model evaluator community.

For questions, partnership inquiries, or technical support:

Access the healthcare AI model evaluator Repository: https://github.com/microsoft/Healthcare-AI-Model-Evaluator
Schedule a demonstration: hlsfrontierteam@microsoft.com

 

The future of healthcare AI depends not only on developing better models, but also on implementing better evaluation. With healthcare AI model evaluator, thorough model evaluation is within reach.

 

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.