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Independent AI Validation: From Black Box to Trusted Evidence

Consulting Virtual
Unique Identifier: c088349b-9996-4542-b637-87afee8cc82f

Service Description

Who Can Benefit: - MedTech Companies Seeking Certification: For independent validation evidence required by notified bodies and regulatory authorities. - Clinical Decision-Makers: For objective assessment of AI tool performance before clinical adoption. - Quality & Regulatory Teams: For comprehensive documentation aligned with EU AI Act, MDR Annex XIV, and relevant AI standards. - Investors & Partners: For independent third-party performance verification as part of due diligence.

Key Features: - Independent, unbiased evaluation of AI model performance on clinical data - Comprehensive quality assessment: accuracy, robustness, fairness, explainability, and failure mode analysis from established assessment framework - Alignment with regulatory frameworks (EU AI Act, MDR, FDA AI/ML guidance) and standards (ISO 24028, CLAIM checklist) - Secure on-premises evaluation – no cloud computing; models and data never leave secure infrastructure - Clinical relevance assessment by domain experts

Possible Applications: - Fairness & Bias Audits: Independent assessment of whether AI models perform equitably across demographic subgroups (age, sex, ethnicity) in clinical and sports populations. - Pre-Submission Validation (MedTech): Independent performance verification of cardiac AI algorithms (e.g., arrhythmia detection) prior to MDR or FDA regulatory submission. - Post-Market Performance Monitoring: Periodic re-validation of deployed AI models to ensure sustained accuracy as patient populations or data acquisition hardware evolve. - Biomechanical AI Benchmarking: Objective validation of movement analysis algorithms against gold-standard motion capture in laboratory and real-world conditions. - Neurological AI Quality Assessment: Stress-testing seizure detection or tremor classification models across diverse patient populations and recording conditions. - Sports & Rehabilitation Algorithm Verification: Validating AI-based injury risk scores, return-to-play classifiers, or performance metrics against clinical expert assessments and ground truth outcomes. - Comparative Algorithm Studies: Head-to-head benchmarking of competing AI solutions on the same clinically curated dataset to support procurement or partnership decisions.

Who We Are: The Fraunhofer Insitute for Integrated Circuits (Fraunhofer IIS) has established the Center for Sensor Technology and Digital Medicine (CEMDIS) in cooperation with the Universitätsklinikum Erlangen and the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) to enhance modern healthcare through innovative sensor technology and digital solutions. This center focuses on integrating innovative medical technologies such as wearables and robotic systems to support medical diagnostics, patient monitoring and evaluating patient-specific therapies by providing digital health solutions für real-life healthcare. Located at the Universitätsklinikum Erlangen, it offers unique infrastructures for the development, integration, and validation of novel health technologies, providing companies opportunities for technological advancements. For more information, visit the Fraunhofer IIS website.

Keywords: MDR Clinical Evaluation AI testing and validation regulatory compliance performance evaluation bias detection bias mitigation Performance Evaluation/Reporting quality assessment
TEF-Health Use Case Domain: all
Provider Logo

Provider & Contact

Provider Organisation Fraunhofer Gesellschaft Zur Forderung Der Angewandten Forschung Ev (Fraunhofer)
Provider Country Germany
Organisation Website https://www.iis.fraunhofer.de/de/ff/sse/health/medical-sensors-and-analytics/analyse-multimodaler-daten.html
Published Email dhs-studien@iis.fraunhofer.de

Pricing is available to registered users. SMEs receive significant state-aid reductions (GBER) — or, depending on the call, free services during the funded project. Sign in or register to see the price for your organisation.

Operational Details

Service Inputs - AI Model / Algorithm or System - Intended Use / Use Case Description / Targeted Clinical Domain - Specification Document / Performance Claims / Known Limitations - Validation Dataset and optionally Testing Procedure - Reference Standard Definition / Gold Standard / Ground Truth - Regulatory Target
Service Outputs - Validation Protocol / Performance Validation Report - Robustness Report - Bias & Fairness Report - Clinical Relevance Evaluation and Improvement Suggestions
Dependencies & Restrictions - Model must be accessible (in black box format) - Documentation must be made accessible (NDA possible)
Comments - The service acts as an independent third-party validation body, providing unbiased evidence that strengthens regulatory submissions and builds trust with clinical end-users. - All computation is performed on secure internal clusters with no cloud dependency – ideal for clients with strict data sovereignty and IP protection requirements. For large-scale validation studies, the FAU Erlangen-Nürnberg HPC cluster is available under institutional data governance (data access agreement necessary). - Validation methodology is designed to anticipate EU AI Act requirements for high-risk AI systems (Annex IV documentation, post-market monitoring readiness). - The clinical co-location at Universitätsklinikum Erlangen enables access to domain experts who assess not just statistical metrics but real-world clinical utility. - This service can be engaged standalone (bring your own model + data), or as the final stage of a full pipeline combining Service 1 (data collection) and Service 2 (model training). - Re-validation after model refinement or retraining cycles can be arranged as a recurring engagement.

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