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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

  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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• 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 performance evaluation

  Multitel (MULTITEL)

  This service provides an independent and reproducible evaluation of AI model performance using well-established quantitative metrics. The evaluation is conducted in a controlled and documented execution environment to ensure traceability and repeatability of results. Performance is assessed on client-provided datasets and models, and associated measurement uncertainties are systematically analyzed and reported. The service delivers a detailed and interpretable evaluation report. All activities are performed under ISO 9001 certified processes.

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• AI performance evaluation based on testing datasets

  Laboratoire National De Metrologie Et D'Essais (LNE)

  To ensure that their solution works accordingly regarding a task, AI provider have to evaluate their system using a specific dataset. The performance obtained on this dataset helps to prove the adequate performance of their AI solution. However, the evaluation process can be often quite hard for an AI provider to do properly: the evaluation dataset needs to be correctly qualified, and the creation of the evaluation protocol as well as the analysis of the results are not easy tasks. This service allows AI provider to benefit of the LNE expertise with a full evaluation of their AI system: using an evaluation dataset created for the test or provided by a partner of the TEF project, an assessment of the performance of the AI system is done, and an analysis of its behavior provided. This work also includes a quality assessment of the evaluation dataset. The scope of the analysis of the evaluation results can include robustness and resilience evaluation, depending of the needs of the SMEs. With this service, the customer will have a full assessment of its solution, allowing them to answer the accuracy requirements of the AI regulation, while also having a full report following all transparency and reproductibility requirements. This service generally takes around 2 months, depending of the needs of the customers.

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