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• 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 Imaging Lab: Development & Validation of Segmentation and Detection Models

  Karolinska Institutet (KI)

  ## Overview This service supports SMEs and researchers in developing, training, and validating AI models for medical imaging applications. Hosted at SMAILE, Karolinska Institutet, it covers segmentation, detection, and classification tasks using CT, MRI, nuclear images, multimodal images, ultrasound, histology, and microscopic images. The pipeline includes data curation, evaluation of labeling strategy, model selection and training, evaluation metric selection, and model performance benchmarking. We provide expertise and support in: - AI model training and optimization for medical imaging - Validation using clinical datasets and standard metrics (using publicly available datasets, datasets available through data agreements, and internal datasets at KI, depending on the case) - Clinical Relevance and Comparison with the state of the art in research and clinical practice - Imaging biomarkers studies for diagnosis, prognosis, and prediction applications ### How can the service help you? The service ensures your imaging AI model performs reliably and is aligned with clinical expectations. Whether you’re entering the pre-clinical testing phase or seeking validation to secure investment or regulatory approval, this service equips you with a rigorous evaluation and feedback report. ### How the service will be delivered? Available both virtually and physically. Imaging data can be reviewed remotely through a secure data transfer process. On-site collaboration is also possible for sensitive datasets or model development, evaluation, and validation. A typical project takes 4–6 weeks. --- ## Additional information ### Provider description SMAILE is the digital health core facility at Karolinska Institute. It offers interdisciplinary support in AI for medical imaging, data analytics, and system validation, partnering with leading institutions under the Swedish TEF-Health node. ### Technical description The imaging model pipeline is extensively validated by using a curated benchmark set and standard evaluation frameworks such as well-established quantification metrics for object detection, image segmentation, and classification tasks.. Annotation quality is reviewed, and model performance is benchmarked against open or reference models. Standard medical image processing tools such as PyDicom, ANTs, and ITK, as well as community-driven open-sourced frameworks such as MONAI, are the core components of our designed pipelines. ### Service customization The service can focus on either model development, evaluation, or both. Datasets can be anonymized and securely shared, or analysis can be conducted in a local sandboxed environment.

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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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