Related services

• Datasets for Diagnostic Evaluation and AI Performance in Medical Specialties

  Univerzitna Nemocnica Martin (University Hospital Martin)

  This service provides tailored datasets for evaluating AI performance in key medical specialties, including sleep disorder diagnostics, gastroenterology, digital pathology, and radiology. The datasets reflect real clinical scenarios, supporting the validation of AI algorithms and assisting organizations in achieving regulatory compliance and clinical reliability. Each dataset includes structured data and annotations designed for training, testing, and comparing AI models in various clinical contexts. Keywords: AI dataset, clinical diagnostics, digital pathology, radiology, gastroenterology, sleep disorders, performance validation.

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• Patient-to-Pipeline: Regulatory-Compliant Clinical Data Collection

  Fraunhofer Gesellschaft Zur Forderung Der Angewandten Forschung Ev (Fraunhofer)

  **Who Can Benefit:** - MedTech & AI Companies: For obtaining high-quality, ethically sourced clinical datasets to fuel algorithm development without navigating hospital bureaucracy. - Clinical Researchers: For systematic, regulation-compliant data collection with full documentation and audit trail. **Key Features:** - End-to-end management of ethical approval processes (ethics committee submission, informed consent design) - Prospective data collection within Universitätsklinikum Erlangen clinical departments - Expert clinical annotation and ground truth labeling by domain specialists - Full GDPR compliance with pseudonymization/anonymization pipelines - Structured data output compatible with downstream AI training and analysis workflows **Possible Applications:** - Wearable Sensor Studies (e.g. Cardiology): Prospective collection of continuous ECG, PPG, or blood pressure data from cardiac patients for digital biomarker development and remote monitoring algorithm training. - Motion & Gait Analysis (Biomechanics): Systematic acquisition of IMU, force plate, and motion capture data in clinical and sports lab settings for movement disorder assessment, rehabilitation tracking, or athletic performance evaluation. - Neurological Signal Acquisition: Collection of EEG, EMG, or tremor sensor data from neurological patient cohorts to support AI-based diagnostic or therapy monitoring tools. - Real-World Evidence Datasets: Longitudinal data collection from ambulatory patients in everyday settings, enabling the development of algorithms that generalize beyond controlled lab environments. - Athletic Performance & Injury Prevention: Structured data gathering during sports science testing protocols to build datasets for injury risk prediction, load monitoring, and return-to-play decision support. - Multi-Modal Clinical Datasets: Combined collection of sensor data, imaging, lab values, and patient-reported outcomes to enable holistic, multi-modal AI model development. **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](https://www.iis.fraunhofer.de/de/ff/sse/health/zentrum-sensorik-medizin.html).

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• AI cybersecurity evaluation

  Laboratoire National De Metrologie Et D'Essais (LNE)

  Evaluation of the AI system regarding its robustness against cybersecurity issues ( risk assessment, secure Data, access Control and Authentication, etc …) This will include the design of test protocols, the realization of tests, the analysis of results and production of a test reports.

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• AI model evaluation and benchmarking on genomic datasets

  Karolinska Institutet (KI)

  ## Overview This service provides a comprehensive evaluation and benchmarking report for SMEs' AI models, leveraging genomic research datasets. Following agreed-upon protocols and recommendations, the AI model is rigorously tested against curated datasets. This occurs in a secure environment managed by Karolinska Institutet. SMEs submit their models to the Swedish TEF Health node under pre-signed agreements, ensuring full confidentiality. The evaluation and benchmarking process produces detailed insights, resulting in a report that supports SMEs in validating and refining their AI solutions. We provide expertise and technical support in the following areas: - Project design & management - AI model evaluation and benchmarking ### How can the service help you? This service helps address uncertainties about your AI model’s performance on external datasets. By providing thorough validation, it demonstrates the maturity and reliability of your model. The resulting report can serve as a valuable tool to build trust and confidence among stakeholders, supporting your efforts to showcase the model’s capabilities. ### How the service will be delivered? The service will be delivered according to established ethical agreements and guidelines, in collaboration with the SME and researchers from the Swedish TEF-Health node. Data usage is facilitated through a secure virtual environment managed by Karolinska Institutet, ensuring the highest standards of data protection. --- ## Additional information ### Provider description The Swedish TEF-Health node is a collaboration between Karolinska Institutet, SciLifeLab and RISE, and is led by Karolinska Institutet. Together, we offer world-leading services with our unique collection of core facilities. We can grant services in expert consulting, virtual- and physical testing in the range of in vivo imaging, ex vivo OMICS, pharmaceutical development, simulated healthcare environments, AI-system validation and development, advanced data analysis and other data-driven life science. ### Technical description The service evaluates AI models in a secure environment using high-performance computing infrastructure optimized for large genomic datasets. Models are tested against curated and annotated genomic datasets using state-of-the-art frameworks such as TensorFlow and scikit-learn. Evaluation metrics, including accuracy, precision, and recall, are calculated to benchmark performance against industry standards and reference models. Encrypted storage and strict access controls ensure data security. ### Service customization The service can be customized according to your specific needs. It may be required to combine this service with other services on offer. --- ## Use case example ### Context A biotech SME specializing in rare diseases has developed an AI model to predict genetic predispositions for a rare neurological disorder. The model was trained on internal datasets but has not been validated on external genomic data. Investors and clinical partners are reluctant to adopt the solution without independent evaluation and benchmarking to ensure its generalizability and maturity. ### Objective To validate the AI model’s accuracy and robustness on external genomic datasets, benchmark its performance against existing solutions, and deliver a detailed evaluation report to gain stakeholder trust and regulatory approval. ### Solution The SME submits its AI model to the Swedish TEF-Health node for evaluation. Using secure infrastructure and curated genomic datasets relevant to rare diseases, the model undergoes extensive testing and benchmarking against industry standards. ### Implementation #### Ethical Agreement The SME enters into an ethical agreement with researchers from the Swedish TEF-Health node, ensuring all data collection and usage complies with GDPR and national Swedish regulations. #### Secure Access Usage of the collected data is facilitated through a secure virtual environment managed by Karolinska Institutet, ensuring the highest standards of data protection. #### Evaluation and Benchmarking The model is assessed using metrics like sensitivity, specificity, and ROC-AUC, focusing on its performance in predicting rare disease risks. #### Outcome A benchmarking report is generated, highlighting the model's strengths, weaknesses, and recommendations for improvement. ### Benefits - **Validation & Credibility**: Independent validation enhances trust among clinical and regulatory stakeholders. - **Competitive Benchmarking**: Aligning with industry standards provides an advantage in the rare disease AI market. - **Model Refinement**: Insights from the report drive improvements for clinical readiness. ### Impact The SME secures stakeholder confidence, accelerates discussions with clinical partners, and positions its AI solution as a trusted tool for rare disease risk prediction, paving the way for market adoption and broader collaborations.

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• AI model evaluation/assessment: Clinical model validation

  Centro Hospitalar De Sao Joao Epe (CHSJ)

  The service offers SMEs expert evaluation and validation of their AI models intended for clinical use. Leveraging the hospital's domain expertise in healthcare and data analytics, this service assesses the accuracy and clinical suitability of AI models in real-world clinical scenarios by assessing models against clinically relevant metrics, benchmarks, and regulatory standards, it ensures their safety, reliability, and effectiveness in real-world healthcare settings.

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• AI model evaluation/assessment: Clinical model validation

  Unidade Local De Saúde De Coimbra EPE (ULS Coimbra EPE)

  The service offers SMEs expert evaluation and validation of their AI models intended for clinical use. Leveraging the hospital's domain expertise in healthcare and data analytics, this service assesses the accuracy and clinical suitability of AI models in real-world clinical scenarios by assessing models against clinically relevant metrics, benchmarks, and regulatory standards, it ensures their safety, reliability, and effectiveness in real-world healthcare settings.

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