Skip to Content

Recommendations on testing and evaluating explainability, robustness, and performance

TEF-Health Service
Consulting Virtual

Service Description

Overview

How can the service help you?

Deploying black-box or fragile AI models creates severe compliance risks, reduces stakeholder trust, and increases the likelihood of unpredictable model failure in clinical practice. This service helps overcome these hurdles by establishing clear testing methodologies.

  • Before: An opaque AI algorithm evaluated only on standard validation metrics, with unknown vulnerability to noise, distribution shifts, or interpretability bottlenecks.
  • After: A robust testing strategy equipped with actionable frameworks for evaluating model decision-making, adversarial resilience, and multi-metric performance.

It enables development teams to build trustworthy AI systems, streamline technical documentation, and align their validation workflows with emerging regulatory and evaluation standards.

How will the service be delivered?

The service is delivered remotely through expert evaluation sessions and technical consultations. Clients provide model architecture specifications, validation data protocols, and system documentation. Our experts assess the setup against established testing frameworks and present targeted recommendations in a technical report and review session.

Additional information

Provider description

Operating from the Department of Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg, we are a service provider node within the TEF-Health consortium. The research group specializes in machine learning, biomedical signal processing, and multimodal sensor synchronization. Our team provides testing infrastructure and scientific support for evaluating medical devices, wearables, and contactless sensing systems.

Technical details

The advisory framework incorporates peer-reviewed methods for evaluating transparent, secure, and reliable machine learning architectures:

  • Input Data Requirements: AI model files, architecture descriptions, dataset specifications, and current validation documentation.
  • Core Tasks & Processing: Audit of model explainability methods (e.g., attribution maps, feature importance metrics), evaluation of robustness testing techniques (e.g., sensitivity to input noise, distribution shifts, adversarial perturbation analysis), and setup of multi-dimensional performance validation metrics.
  • Outputs: A comprehensive recommendation report detailing concrete protocols for testing explainability, stress-testing model robustness, and setting up rigorous performance benchmark pipelines.
  • Scientific Reference: Aligned with peer-reviewed methodologies for reliable AI evaluation (DOI: 10.1007/s10489-023-04532-5).

Service customization

Clients can tailor the advisory scope to focus on specific testing pillars based on project priority such as focusing exclusively on Explainable AI (XAI) validation to increase clinical transparency or prioritizing adversarial robustness testing for edge-case failure detection.

Offerings: Development of conformity evaluation standards, protocols & tools
Provider Logo

Provider & Contact

Provider Country Germany
Billing: per hour
Full Price 120 EUR
Reduced Price No discount can be provided
Pricing Detail

The price by hour is an estimate.

Operational Details

Service Inputs AI Model + Documentation
Service Outputs Recommendations on Explainability/Robustness/Performance
Certification Support None