Virtual services

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.

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.

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.

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.

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.

Laboratoire National De Metrologie Et D'Essais (LNE)

Based on the domain of the medical device or robot provided by the customer, the simulation will create a test environment relevant to the system to be evaluated using a video-projection system. The service will be provided using the LE.IA Immersion platform, a platform dedicated to the evaluation of AI. It consists of a video-projection system that makes it possible to reconstitute an "artificial nature" and to immerse the systems to be characterised (personal assistance robots, intelligent cameras, civil and military intervention robots, etc.) in a simulated dynamic reality. It allows the system (robot, camera, etc.) to be subjected to a multitude of test scenarios and to evaluate its reactions in a controlled environment.

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.

Laboratoire National De Metrologie Et D'Essais (LNE)

The system provided by the customer, for instance a medical device using an AI module, will be tested using a simulated environment. The service will be provided using the LE.IA Simulation, a fully simulated test environment (the robot or the medical device and its operating environment are simulated). It allows to generate a very large number of test scenarios that are not feasible in a physical environment.

Laboratoire National De Metrologie Et D'Essais (LNE)

AI resilience evaluation: evaluation of an AI system using a test dataset. This dataset will contain erroneous data designed to disturbed the behavior of the AI system. The general methodology will include the design of test protocols, the realization of tests, the analysis of results and production of a test reports.

Laboratoire National De Metrologie Et D'Essais (LNE)

Evaluation of an AI system using a test dataset augmented with artificial data. The new data results in a wide coverage of the operating range and addition of artificial noise based on the nature of the data processed by the AI system. The LNE data augmentation tools can apply physically-informed transformations to visual data and timeseries data. The visual transformations include for instance: gaussian (electrical) and poisson (thermal) noise, gaussian blur (focus issues), loss of pixels, lines and columns (CCD failures). The timeseries transformations include random gaussian, laplace or uniform noises.

HUMANI SC - Hospital network (HUMANI)

Deployment and evaluation of AI solutions (algorithms, applications, etc.) in a secure IT environment (servers, virtual machines, etc.) within the hospital. This service, provided by clinical and IT experts, includes a technical evaluation to ensure interoperability, accessibility, and interpretability with existing IT systems and a comprehensive evaluation within real-world clinical environments.

Centre D'Excellence En Technologies De L'Information Et De La Communication (CETIC)

Logistic & Process Optimisation: Provide expertise on how to address large scale logistic, scheduling, and other combinatorial optimisation problems using constraint-based local search. CETIC has developed a routing optimization algorithm called RoutaR. It is based on the oscar.cbls optimization engine and on GraphHopper cartography. Both are open source. Method Description: It inputs a routing problem, expressed in a Json file format. It typically contains a set of geographic locations, a description of a fleet of vehicles and a set of constraints. It produces a routing solution; a planning of each vehicle of the considered fleet that mentions their tasks and geographic locations Method reference: https://www.cetic.be/RoutaR-outil-de-planification-performant-et-facilement-adaptable

Centre D'Excellence En Technologies De L'Information Et De La Communication (CETIC)

The collection of data from various sources (devices, files, interconnected services, etc.) is a complex step and typically falls outside the core business of medical sector companies, which aim to focus on the analysis of this data. How can the service help you? The CETIC provides access to a highly customizable catalog of tools for engineering medical data collection, storage and analysis. These tools enable : (a) automation of the collection of heterogeneous medical data from virtually any type of source (like IoT, API, files, remote repositories or databases, propriatary equipments, etc.) and its mapping to fully-customisable data representations enriched with semantics (DMWay tool) (b) a scalable and persistent data storage (c) easy yet powerful data analysis (TSANO tool) How the service will be delivered? It depends on the customer's need Method reference: https://asset.cetic.be/en/dmway/ https://www.cetic.be/analyse-prescriptive-au-service-de-industrie40

Universita Degli Studi Di Pavia (UNIPV)

This service offers the construction and simulation of brain digital twins for neurological and neuropharmacological applications. The simulations are typically applicable to single subject data for developing personalized medicine and pharma. Method description: Raw structural and functional data is preprocessed and analyzed to create brain connectivity data. The computational framework is tuned against experimental data to simulate brain dynamics and optimize physiological parameters, e.g., excitatory and inhibitory activity. In order to obtain personalized digital brain twin single-subject data must be used. Method reference: Local method respecting General Data Protection Regulation (GDPR)

HUMANI SC - Hospital network (HUMANI)

HUmani cleans and annotates clinical datasets from external providers.

HUMANI SC - Hospital network (HUMANI)

Assessment of AI solutions within real-world clinical environments by healthcare and IT professionals. This service covers the entire testing lifecycle: from formative assessments (iterative clinician feedback to refine the product and its integration) to summative validation (structured performance testing against predefined KPIs). It ensures AI solutions are safety and clinically accurate, and optimized for user adoption, workflow efficiency and security.

Centre D'Excellence En Technologies De L'Information Et De La Communication (CETIC)

Cyber-Physical Systems Cybersecurity Testing ensures cyber-physical systems (CPS), such as robotic systems, comply with security requirements and standards (such as Cyber Resilience Act). Security testing includes security functional testing of the whole system, and vulnerability scanning of the software components. The overall goal is to release your product without known important vulnerabilities. How can the service help you? Our service is based on a custom platform that automates vulnerability scanning, penetration testing and security functional testing, with the possibility to integrate our platform in your DevSecOps activities for full security activities automation. The focus on cyber-physical systems takes the form of support for physical interfaces and buses/protocols, with possible attacks on RF signals (GPS or WiFi), for example. Our offer differentiates from existing ones with a unique risk-based approach, where a security risk analysis drives the whole process of security testing, and by the use of open-source tools to avoid vendor lock-in. The method is based on existing standards : PTES, Etsi, NIST, FDAM... How the service will be delivered? Based on information provided by the customer about their system, this service offers to use our Automated Cybersecurity Testing platform, tools and method to define and perform cybersecurity tests, either on site or in our lab. Optionally, we can integrate our platform in your DevSecOps chain for full automation of security activities. Optionally, we can perform the security risk analysis that is used as input for the whole process. We will provide complete test reports as well as recommendations to lower the residual risk level and attack surface of your system. Together with the risk analysis, they can be used as a complete set of evidences towards authorities and customers. Service deployment: The service is deployed either on site or in our lab. Our test platform is made of several components : a server that stores all relevant information and generate the reports, and test workstations that can be used in our lab or at customer sites directly. Service standards: PTES, Etsi, NIST, FDAM...

Karolinska Institutet (KI)

## Overview This service provides a complete organisation or re-organisation of your datasets as well as guidance in selecting the most appropriate metadata management methodology. We offer data cleaning (e.g., renaming, annotation, formatting, and duplicate removal), quality control, and anonymization tailored to SME needs. This service aims to provide assistance based on a dataset management plan. We follow community data and metadata standards in place and offer curated, up-to-date guidance based on FAIR principles (Findable, Accessible, Interoperable and Reusable) whenever possible. We have expertise in data and metadata standards in many life science fields, including, more specifically, imaging (PET, MRI, MEG, EEG) and structural data, genomics, metabolomics, and proteomics data. Expertise in legal and ethical aspects of dataset management can also be provided. We provide expertise and technical support in the following areas: - Project design & management - Legal & ethical support - Data cleaning, annotation, anonymisation ### How can the service help you? This service helps SMEs improve the organization and usability of their datasets by providing data cleaning, quality control, and anonymization tailored to their needs. With expert guidance on metadata management and adherence to FAIR principles, it ensures your data is reliable, compliant, and ready for effective use, building trust and confidence in your data-driven initiatives. ### 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 processing 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 restructures and processes datasets within a secure computing environment utilizing advanced data curation and metadata management frameworks. Data preprocessing operations, including nomenclature standardization, semantic annotation and format normalization are executed in compliance with domain-specific ontologies and if possible FAIR principles. Quality assurance protocols are implemented to validate data integrity and compliance. Metadata schemas are optimized for interoperability and reuse, leveraging established standards such as RDF and JSON-LD. ### 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 life science SME specializing in metabolomics has developed a novel pipeline for identifying biomarkers in rare metabolic disorders. They have generated extensive LC-MS/MS datasets from patient samples across multiple studies. However, the datasets are stored in a mix of proprietary formats, lack harmonized metadata and do not meet the submission requirements of repositories like MetaboLights. This situation delays the publication of their findings, limiting their visibility and ability to secure collaborations or funding for further pipeline validation. ### Objective To clean, harmonize and annotate the SME’s LC-MS/MS datasets according to MetaboLights requirements. Ensure compliance with FAIR principles to facilitate immediate repository submission and support future scalability. ### Solution The SME engages with the Swedish TEF-Health node to reorganize and optimize their metabolomics datasets for repository submission. Experts provide tailored data cleaning, format conversion, and metadata harmonization, ensuring compatibility with repository standards and enabling wider reuse of the data. ### 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. #### Data Processing and Format Conversion The SME’s raw datasets, stored in various proprietary formats, are converted into open formats such as mzML and mzTab, which are compatible with repository requirements. During this process, quality control measures, including noise filtering and peak annotation, are applied to improve data integrity and reliability. #### Metadata Harmonization Metadata schemas based on MetaboLights standards are created. These schemas incorporate essential details about study design, sample preparation and instrument parameters. Ontology based annotation is applied to harmonize metadata across all datasets, ensuring consistency and compliance with repository guidelines. ### Benefits - **FAIR Data Management**: Enhances SMEs’ ability to manage and share clinical trial data effectively while ensuring interoperability and quality. - **Stakeholder Credibility**: A well-structured data management plan builds trust with regulators, funders, and collaborators. - **Regulatory Compliance**: Ensures adherence to ethical and legal standards, reducing data handling risks. - **Data Sustainability**: Supports long-term usability and scalability, enabling future research opportunities. ### Impact The SME’s biomarker discovery pipeline gains recognition as a reliable and validated tool in the rare disease research community. Their repository-submitted data fosters new collaborations with academic researchers and industry stakeholders, accelerating the translation of their findings into clinical applications.

Karolinska Institutet (KI)

## Overview This service empowers projects to efficiently plan and manage their data through a structured system and expert support. The system is built on the Data Stewardship Wizard (DSW), a tool developed by ELIXIR, a European life science intergovernmental organization, and operated by the SciLifeLab Data Centre. Expert data stewards provide personalized guidance, leveraging best practices and resources from the ELIXIR RDMkit and SciLifeLab’s data guidelines. We provide expertise and technical support in the following areas: - A comprehensive data management planning system with ready-to-use templates. - Expert advice and assistance in data stewardship and management, tailored to your project’s needs. This service ensures that your data management plan aligns with FAIR principles and established standards, promoting high-quality data practices and enabling better long-term data usability. ### How can the service help you? This service helps SMEs and research projects streamline their data management planning by providing access to a robust, user-friendly system and expert guidance. With tailored templates and best-practice guidelines, it ensures that your data management plan complies with established standards. By addressing critical aspects of data stewardship early in the project lifecycle, the service improves data quality, facilitates compliance, and enables better collaboration and long-term reuse of data, ensuring your project’s data is FAIR (Findable, Accessible, Interoperable, and Reusable). ### 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. --- ## 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 employs the DSW platform to create structured, FAIR-compliant data management plans. It provides customizable templates, enabling collaborative workflows for stakeholders. Expert guidance ensures plans meet domain-specific standards promoting data interoperability and long-term usability. ### Service customization The service can be customized according to your specific needs. --- ## Use case example ### Context A research-focused SME is preparing to launch a multi-center clinical trial for a new biomarker discovery project. The trial will generate extensive genomic, proteomic, and clinical datasets across multiple sites. However, the SME lacks a clear and structured approach to plan how the data will be managed, shared, and stored. Without a robust data management plan, they risk non-compliance with regulatory requirements, inefficient data handling, and challenges in ensuring data interoperability and long-term usability. ### Objective To develop a comprehensive data management plan tailored to the SME’s clinical trial needs, using the DSW platform. Ensure the plan addresses data collection, storage, sharing, and long-term preservation while aligning with FAIR principles and regulatory requirements. Deliver a finalized plan that supports seamless collaboration across trial sites, enhances data quality, and ensures compliance with international standards for research data management. ### Solution The SME collaborates with the Swedish TEF-Health node to create a robust data management plan using the DSW platform. Expert data stewards guide the SME in utilizing customizable templates. The plan is developed collaboratively, addressing key areas such as data collection, metadata standards, storage solutions, and sharing protocols. Regulatory and ethical considerations, including GDPR compliance, are also incorporated. The finalized data management plan is reviewed and optimized to facilitate smooth implementation across all trial sites, ensuring scalability and long-term usability of the data. ### Implementation #### Ethical Agreement The SME enters into an ethical agreement with researchers from the Swedish TEF-Health node, ensuring that all data handling comply with GDPR and Swedish national regulations. #### Data Management Planning Using the DSW platform, the SME collaborates with data stewards to develop a detailed data management plan. The plan incorporates customized templates addressing key elements. #### Review and Optimization The draft data management plan is reviewed collaboratively with stakeholders, ensuring it meets the specific requirements of the clinical trial and complies with all regulatory standards. ### Benefits - **FAIR Data Management**: Enhances SMEs’ ability to manage and share clinical trial data effectively while ensuring interoperability and quality. - **Stakeholder Credibility**: A well-structured data management plan builds trust with regulators, funders, and collaborators. - **Regulatory Compliance**: Ensures adherence to ethical and legal standards, reducing data handling risks. - **Data Sustainability**: Supports long-term usability and scalability, enabling future research opportunities. ### Impact By ensuring high-quality, interoperable data, the SME builds trust with regulatory bodies, funding agencies, and research collaborators. This structured approach to data management fosters new collaborations, accelerates research outcomes, and positions the SME as a leader in clinical trial data handling. The long-term usability of their data enables broader scientific impact, enhancing their reputation and paving the way for future innovations.

Centre Hospitalier Universitaire De Rennes (CHU RENNES)

This service aims to formalise and consolidate the SME's request (thanks to our expertise in clinical data warehouse and clinical investigation) and to specify services that we provide which will allow to conduct the study. Eventually, complementary services provided by other partners can be identified. This service includes a consultation session of approximately 2 hours to discuss the proposed project, its key aspects, its direction, and its feasibility. We will leverage our expertise in the reuse of real-world health data, addressing aspects such as data availability, feasibility, regulatory processes, and study methodologies.​ Method Description: The service includes : - Contact form - Documentation of the requirement - Specification document - Identification of the Medical Service Rendered - Identification of inclusion factors, epidemiological factors, factors linked to objectives, confounding factors - Scope : CDWH/CDWH network/SNDS/ academic labs or other TEF partners - Choice/identification of the project manager/sponsor Method reference: Local method respecting General Data Protection Regulation (GDPR), EU regulations 2017/745 and 2020/561 on medical devices, AI Act and Good Clinical Practice (GCP)

Centre Hospitalier Universitaire De Rennes (CHU RENNES)

This service allows the validation of the clinical question and the feasibility of the SME's request. To be more specific, it first includes a validation of the clinical question with a clinical expert. Then, a pre-screening is conducted to assess data availability, leveraging the expertise of our data scientists, which leads to the production of a feasibility report. Prerequisite: our service "SME initial consulting" must have been previously conducted.​ Method Description: The service includes : - Pre-screening [mandatory] - Feasibility study [mandatory] Method reference: Local method respecting General Data Protection Regulation (GDPR), AI Act and Good Clinical Practice (GCP)

Centre Hospitalier Universitaire De Rennes (CHU RENNES)

This support service offer to companies scientific and methodological advice throughout the project. It includes regular progress meetings and ongoing support from a project manager. This may also include support in drafting the research plan and contacts with healthcare professionals. This service provides comprehensive support for companies' R&D projects, with personalized follow-up tailored to their needs, while ensuring the clinical validity of their approaches to exploit clinical warehouse data.​ Details :​ - Meeting with a Clinical Research Project Manager from the Clinical Data Center. The project manager will ensure project management in our institution and key steps related to clinical data warehouse exploitation (e.g., submission to local ethics and scientific commitee).​ - Regular meetings including a group of experts specialized in medicine, clinical data science, clinical research and health technology. Pricing will depend on the mobilized expertise and regularity of the meetings.​ - The opportunity to have your research protocol reviewed and evaluated by experts. This service includes one submission. New submission will result in a re-evaluation of the service charges.​ Prerequisite : our service "Feasibilty study" must have been previously conducted.​ Method Description: The service includes a minimal mandatory task set. Optional tasks can be jointly selected depending on the SME needs and available resources: - Regular progress meetings (fortnightly/monthly) [mandatory] - Review and advice on research plan writing, testing and evaluation [optional] - Review and analysis of research plan by clinicians [optional] Method reference: Local method respecting General Data Protection Regulation (GDPR), EU regulations 2017/745 and 2020/561 on medical devices, AI Act and Good Clinical Practice (GCP)

Centre Hospitalier Universitaire De Rennes (CHU RENNES)

This service focuses on providing support in analyzing the legal and ethical framework, which involves helping to understand the laws and regulations in force, as well as the ethical principles and values applicable for clinical data warehouse exploitation. It may involve compliance assessment, particularly with regard to RNIPH and legal compliance, which consists in ensuring that the activities in question comply with the applicable laws and rules. It may include assistance with the construction and filing of regulatory dossiers (CEREES, CNIL, etc.). It offers the opportunity to have several documents reviewed and evaluated by experts as some help for CESREES and CNIL submission. This service is tailored by the SME needs.​ Details:​ - An initial consultation meeting with a legal expert of the hospital to clarify the requirements for the CESREES and CNIL submissions.​ - One feedback session and an annotated report for each document (study protocol, data protection impact assessment GDPR, etc.) that the SME needs to revise are included. New submission will result in a re-evaluation of the service charges. Prerequisite : our service "Feasibility study" must have been previously conducted. Method Description: Depending on the SME needs and available resources, this service may include all or part of the following task list: - Analysis of legal and ethical framework (Ethical committee) [optional] - Compliance assessment (RNIPH, legal compliance, etc.) [optional] - Assistance and advice on building regulatory files (CESREES, CNIL, MR004) [optional] Method reference: Local method respecting General Data Protection Regulation (GDPR), Comité d’Expertise pour les Recherches, les Etudes et les Evaluations dans le domaine de la Santé (CEREES), Commission Nationale de l’Informatique et des Libertés (CNIL), MR-004 reference methodology requirements and EU regulations 2017/745 and 2020/561 on medical devices

Centre Hospitalier Universitaire De Rennes (CHU RENNES)

This service provides access to health data from Clinical Data Warehouses of CHU Rennes or/and CHU Grenoble-Alpes and from other clinical data warehouse networks. It includes datamart building (cohort and document selection, mapping to standard...) and data qualification (extraction of clinical variables, data cleaning, clinical consistency checking...) steps. In this way, the service offers companies an opportunity to access healthcare data and/or descriptive statistical analyses.​ This service involves only structured data extraction and pre-processing. In cases where the data is unstructured (e.g., requiring natural language processing for text, feature extraction from images, etc.), a specialized algorithm development service will be required (see our service "Algorithm development" for more details).​ Prerequisite : To have data access, it is essential to have CNIL (Commission Nationale de l'Informatique et des Libertés) approval, a favorable opinion from the Scientific and Ethics Council (CSE), and a signed contract between SME and hospital. Furthermore, our services "Feasibility study" and "Methodological advising" must have been previously conducted.​ Method Description: The service includes a minimal mandatory task set. Optional tasks can be jointly selected depending on the SME needs and available resources: - Datamart building - Patient selection (cohort) [mandatory] - Selection of data of interest [mandatory] - Mapping to FHIR/OMOP standards [optional] - Data qualification steps - Extraction of variables of interest [optional] - Data cleaning [mandatory] - Clinical consistency check by expert [mandatory] - Annotation/segmentation [optional] - Descriptive statistical study [optional] - Confidentiality control [mandatory] - Matching with other databases (i.e., SNDS) [optional] - Deployment of methods on another warehouse [optional] - Datamart availability on Hospital platform (CHU platform) [Standard offer] Method reference: Based on a scientific reference or a method designed by CHU Rennes and its partners (LTSI, LaTIM, etc.), as well as on methodologies provided by WP6. Provision by respecting General Data Protection Regulation (GDPR), AI Act, Good Clinical Practice (GCP) and french legal framework

Centre Hospitalier Universitaire De Rennes (CHU RENNES)

This service tends to offer a scalable platform to access dataset by the SME taking into account its infrastructure requirements and french legal framework.​ Prerequisite : The dataset must exclusively originate from our data warehouse. Method Description: Depending on the SME needs and available resources, this service may include all or part of the following task list: - Deployment of the dataset on an environment outside CDW Method reference: Provision by respecting General Data Protection Regulation (GDPR), AI Act, Good Clinical Practice (GCP) and french legal framework

Centre Hospitalier Universitaire De Rennes (CHU RENNES)

This service offers method development for tasks at different processing levels (pre-processing, feature extraction, statistical, machine learning and/or deep learning algorithms). In short, it offers support for the full exploitation of data at all processing stages. With this service, companies can benefit from clinical and data science expertise to facilitate the entire healthcare data processing process, including raw data cleaning, extraction of relevant features, statistical analysis and implementation of advanced algorithms. Specific capabilities include text processing using regular expression (regex) filtering or NLP techniques, feature extraction from images, physiological signal through both Machine Learning and Deep Learning algorithms.​ Prerequisite : The dataset must exclusively originate from our data warehouse. Our service "Data provision and analysis" must simultaneously be conducted. Method Description: The service includes: - Drafting of functional specifications [mandatory] - Development of data exploitation methods [mandatory] Method reference: Based on scientific reference or methods designed by CHU Rennes and its partners (LTSI, LaTIM, etc.), as well as on methodologies provided by WP6. Implemented methods will integrate General Data Protection Regulation (GDPR), AI Act, Good Clinical Practice (GCP) insights.

Centre Hospitalier Universitaire De Rennes (CHU RENNES)

The role of this service is to support the promotion and exploitation of research undertaken from an academic point of view. In particular, this involves proofreading and writing scientific articles, as well as participating in scientific conferences. The scientific commercialization service is therefore essential to ensure the dissemination and visibility of research results.​ Prerequisite: The study must have been conducted throught our other services. Method Description: Depending on the SME needs and available resources, this service may include all or part of the following task list: - Scientific article writing [optional] - Proofreading and advice on writing scientific articles [optional] - Participation in scientific events (conferences, etc...) [optional] Method reference: Based on scientific and clinical expertise

Centre Hospitalier Universitaire De Grenoble (CHUGA)

This service allows to validate the clinical question, the feasibility of SME demand. To be more specific, it first includes a validation of the clinical question with a clinical expert. Then, a pre-screening is conducted to assess data availability, leveraging the expertise of our data scientists, which leads to the production of a feasibility report. Prerequisite: If the need remains to be precised, our service "Initial Consultation for SMEs" must be conducted before starting the feasibility study.​ Method Description: The service includes :     Pre-screening [mandatory]     Feasibility study [mandatory] Method reference: Local method respecting General Data Protection Regulation (GDPR), AI Act and Good Clinical Practice (GCP)

Centre Hospitalier Universitaire De Grenoble (CHUGA)

This support service offer to companies scientific and methodological advices throughout the project. It includes regular progress meetings and ongoing support from a project manager. This may also include support in drafting the research plan and contacts with healthcare professionals. This service provides comprehensive support for companies' R&D projects, with personalized follow-up tailored to their needs, while ensuring the clinical validity of their approaches to exploit clinical warehouse data.​ Details :​    - Meeting with a Clinical Research Project Manager from the Clinical Data Warehouse. He will ensure project managment in our institution and key steps related to clinical data warehouse exploitation (e.g., submission to local ethics and scientific comitee).​    - Regular meetings including a group of experts specialized in medecine, clinical data science, clinical research and health technology. Pricing will depend on the mobilized expertise and regularity of the meetings.​    - The opportunity to have your research protocol reviewed and evaluated by experts. This service includes one submission. New submission will result in a re-evaluation of the service charges.​ Prerequisite : our service "Feasabilty study" must have been previously conducted.​ Method Description: The service includes a minimal mandatory task set. Optional tasks can be jointly selected depending on the SME needs and available ressources:     Regular progress meetings (fortnightly/monthly) [mandatory]     Review and advice on research plan writing, testing and evaluation [optional]     Review and analysis of research plan by clinicians [optional] Method reference: Local method respecting General Data Protection Regulation (GDPR), EU regulations 2017/745 and 2020/561 on medical devices,  AI Act and Good Clinical Practice (GCP)

Centre Hospitalier Universitaire De Grenoble (CHUGA)

This service focuses on providing support in analyzing the legal and ethical framework, which involves helping to understand the laws and regulations in force, as well as the ethical principles and values applicable for clinical data warehouse exploitation. It may involve compliance assessment, particularly with regard to RNIPH and legal compliance, which consists in ensuring that the activities in question comply with the applicable laws and rules. It may include assistance with the construction and filing of regulatory dossiers with French regulatory authorities*. It offers the opportunity to have several documents reviewed and evaluated by experts as some help for deposit. This service is tailored by the SME needs.​ Details:​     An initial consultation meeting with a legal expert of the hospital to clarify the requirements for the regulatory submissions.​     One feedback session and an annotated report for each document (study protocol, data protection impact assessment GDPR, etc.) that the SME needs to revise are included. New submission will result in a re-evaluation of the service charges. Prerequisite : our service "Feasability study" must have been previously conducted. Method Description: Depending on the SME needs and available ressources, this service may include all or part of the following task list:     Analysis of legal and ethical framework by our Institutional Review Board (IRB) [optional]     Compliance assessment (Category of research, legal compliance, etc.) [optional]     Assistance and advice on building regulatory files [optional] Method reference: Local method respecting General Data Protection Regulation (GDPR), *French regulatory autorities :     Comité Ethique et Scientifique pour les Recherches, les Etudes et les Evaluations dans le domaine de la Santé (CESREES),     Commission Nationale de l’Informatique et des Libertés (CNIL), MR-004 reference methodology requirements and EU regulations 2017/745 and 2020/561 on medical devices

Centre Hospitalier Universitaire De Grenoble (CHUGA)

This service provides access to health data from Clinical Data Warehouses of CHU Grenoble-Alpes. It includes datamart building (cohort and document selection, mapping to standard...) and data qualification (extraction of clinical variables, data cleaning, clinical consistency checking...) steps. In this way, the service offers companies an opportunity to access healthcare data and/or descriptive statistical analyses.​ This service involves only structured data extraction and pre-processing. In cases where the data is unstructured (e.g., requiring natural language processing for text, feature extraction from images, etc.), a specialized algorithm development service will be required (see our service "Algorithm development" for more details).​ An extension of the study to other clinical data warehouses from the TEF consortium can be considered in a second time. Prerequisite : To have data access, it is mandatory to fullfil the regulatory package asked by the French regulatory authorities* and a signed contract between the SME and the hospital. Furthermore, our services "Feasability study" and "Methodological advising" must have been previously conducted.​ Method Description: The service includes a minimal mandatory task set. Optional tasks can be jointly selected depending on the SME needs and available ressources: - Datamart building - Patient selection (cohort) [mandatory] - Selection of data of interest [mandatory] - Data qualification steps - Extraction of variables of interest [optional] - Data cleaning [optional] - Clinical consistency check by expert [mandatory] - Annotation/segmentation [optional] - Descriptive statistical study [optional] - Confidentiality control [mandatory] - Matching with other databases (i.e., SNDS) [optional] - Deployment of methods on another warehouse [optional] - Datamart availability on Hospital platform (CHU platform) [Standard offer] Method reference: Based on a scientific reference or a method designed by CHU Grenoble Alpes, as well as on methodologies provided by WP6. Provision by respecting General Data Protection Regulation (GDPR), AI Act, Good Clinical Practice (GCP) and french legal framework *Regulatory package: - Approval from the CNIL (Commission Nationale de l'Informatique et des Libertés) - Favorable scientific opinion (provided internally in the hospital by a scientific committee) - Favorable ethical assessment (provided internally in the hospital by an Institutional Review Board (IRB))

Centre Hospitalier Universitaire De Grenoble (CHUGA)

This service tend to offer a scalable platform to access dataset by the SME taking into account its infrastructure requirements and french legal framework.​ Prerequisite : The dataset must exclusively originate from our data warehouse. Method Description: Depending on the SME needs and available ressources, this service may include all or part of the following task list: - Deployment of the dataset on an environment outside Clinical data Warehouse Method reference: Provision by respecting General Data Protection Regulation (GDPR), AI Act, Good Clinical Practice (GCP) and French legal framework

Centre Hospitalier Universitaire De Grenoble (CHUGA)

This service offers method development for tasks at different processing levels . With this service, companies can benefit from clinical and data science expertise to facilitate the entire healthcare data processing process, including raw data cleaning, extraction of relevant features (pre-processing), statistical analysis and implementation of advanced algorithms in some cases. In particular, we provide specific capabilities including text processing using NLP techniques, feature extraction from images through both Machine Learning and Deep Learning algorithms.​ Prerequisite : The dataset must exclusively originate from our data warehouse. Our service "Data provision and analysis" must simultaneously be conducted. Method Description: The service includes: - Drafting of functional specifications [mandatory] - Development of data exploitation methods [mandatory] Method reference: Implemented methods will integrate General Data Protection Regulation (GDPR), AI Act, Good Clinical Practice (GCP) insights.

Centre Hospitalier Universitaire De Grenoble (CHUGA)

This service will offer the possibility for SME to navigate interactively throught datasets thanks to an API developed within the CHUGA. It allows to select interactivly data and to visualize it thanks to BI tools. A short period of training is needed to be fully autonomous and able to exploit the full potential of the app. Method Description: Deployment of an API on a secure environment to navigate and vizualize interactively data.

Centre Hospitalier Universitaire De Grenoble (CHUGA)

The role of this service is to support the promotion and exploitation of research undertaken from an academic point of view. In particular, this involves proofreading and writing scientific articles, as well as participating in scientific conferences. The scientific commercialization service is therefore essential to ensure the dissemination and visibility of research results.​ Prerequisite: The study must have been conducted thought our other services. Method Description: Depending on the SME needs and available ressources, this service may include all or part of the following task list: - Scientific article writing [optional] - Proofreading and advice on writing scientific articles [optional] - Participation in scientific events (conferences, etc...) [optional] Method reference: Based on scientific and clinical expertise

Centro Hospitalar De Sao Joao Epe (CHSJ)

The service offers SMEs access to high-quality, annotated clinical datasets with relevant clinical for testing purposes, also for re-train algorithm models. These annotated datasets enable SMEs to validate their products, such as machine learning algorithms or medical devices, using real-world clinical data.

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

The service provides access to high-quality, annotated clinical datasets that have been curated and enriched to ensure relevance, quality, and suitability for healthcare research, development, and innovation, that are relevant for testing purposes and also for retraining algorithm models. These annotated datasets enable SMEs to validate their products, such as machine learning algorithms or medical devices, using real-world clinical data.

Centre Hospitalier Universitaire De Grenoble (CHUGA)

This service aims to formalise and consolidate the SME request (thanks to our expertise in clinical data warehouse and clinical investigation) and to specify services that we provide which will allow to conduct the study. Eventually, complementary services provided by other partners can be identified. This service includes a consultation session of approximately 2 hours to discuss the proposed project, its key aspects, its direction, and its feasibility. We will leverage our expertise in the reuse of real-world health data, addressing aspects such as data availability, feasibility, regulatory processes, and study methodologies.​ Method Description: The service includes :     - Contact form     - Documentation of the requirement     - Specification document     - Identification of the Medical Service Rendered     - Identification of inclusion factors, epidemiological factor, factors linked to objectives, confounding factors     - Scope : Clinical data warehouse/French health insurance medico-administrative data (SNDS)/ academic labs or other TEF partners     - Choice/identification of the project manager/sponsor Method reference: Local method respecting General Data Protection Regulation (GDPR), EU regulations 2017/745 and 2020/561 on medical devices, AI Act and Good Clinical Practice (GCP)

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

This service offers SMEs access to curated clinical datasets along with expert guidance in selecting relevant variables for their specific use cases. Levaraging the hospital's extensive data resources and by thoroughly understanding the clinical requirements and objectives of each study, it ensures that the chosen variables are relevant, high-quality, and fit for purpose, thereby enabling more robust, efficient, and clinically meaningful analyses. This service assists SMEs in identifying the most pertinent variables from clinical datasets to support their product development efforts.

Centro Hospitalar De Sao Joao Epe (CHSJ)

This service offers SMEs access to curated clinical datasets along with expert guidance in selecting relevant variables for their specific use cases. Levaraging the hospital's extensive data resources and by thoroughly understanding the clinical requirements and objectives of each study, it ensures that the chosen variables are relevant, high-quality, and fit for purpose, thereby enabling more robust, efficient, and clinically meaningful analyses. This service assists SMEs in identifying the most pertinent variables from clinical datasets to support their product development efforts.

Spms - Shared Services Of The Ministry Of Health, E.P.E

This service offers SMEs access to AI test datasets based on defined criteria, existing legislation and models E.g. REST and SOAP WEB Services Method Description: Analysis of the data requested including feasibility, ethical requirements, meeting with the company, data extraction and validation and secure access. Method reference: Data privacy impact assessment (https://www.cnil.fr/en/privacy-impact-assessment-pia), Regulation (EU) 2016/679 GDPR, Portuguese Law No.58/2019

Univerzitna Nemocnica Martin (University Hospital Martin)

Provisioning of datasets from gastroenterology tests and procedures and consultancy related to data processing and development of AI diagnostic asistance tools Method Description: The service will provide diagnostic datasets collected during gastroenterology tests and procedures for development of AI based diagnostic assistance tools. Medical professionals will evaluate results obtained by using developed tools to assist the diagnostic procedures and tests. Method reference:

Laboratoire National De Metrologie Et D'Essais (LNE)

With the development of AI models in most sectors of society, the importance of the data used to train and test these models has a been an important concern for the AI supplier. Understanding the quality of the datasets used, especially in sector such as the medical domain where data can be hard to get, is thus a prerequesite to ensure the correct development of AI models and building trust regarding these approach for society. This service aims to answer this challenge by assessing the quality of datasets provided by the client. The features of the datasets that will be analyzed are based on agreed definition by the AI community: completeness, balance, diversity, accuracy... The service will help the client that want to ensure the quality of a dataset and understand the features of the data, thus answering the requirements regarding data quality of the AI regulation. A report will be provided to the customer at the end of the service, explaining the methodology and detailling all the analysis and conclusions of the study. The service generally take around one month to be provided once the data has been delivered to LNE. Work is generaly done directly on LNE infrastructure, but in case of confidentiality requirements, it is possible to adapt the work.

Laboratoire National De Metrologie Et D'Essais (LNE)

Development of an evaluation method for the AI solution: given the specifications and context of application of the AI system, the test will provide the evaluation plan to assess the performance of the system, in order for the customer to realize its own evaluation. This service will be based on the expertise of the LNE in creating evaluation plan given an AI system. The evaluation plan will contain the specification of the evaluation tasks, the creation of the test data, the metrics, and the evaluation protocol. All these elements will be used by the customers to conduct the evaluation of the AI system.

Eucaim

Cancer Image Europe provides a robust, trustworthy platform for researchers, clinicians, and innovators to access diverse cancer images, enabling the benchmarking, testing, and piloting of AI-driven technologies. EUCAIM Platform integrates a dashboard, a catalogue and a federated searching environment to discover Medical Imaging data related to cancer from a distributed federation of data holders. EUCAIM provides in some nodes processing capacity to securely access the data. EUCAIM is mainly intended for Data scientists to develop, validate or improve AI models or image postprocessing tools. The platform is accessible in https://dashboard.eucaim.cancerimage.eu/

Univerzitna Nemocnica Martin (University Hospital Martin)

The service provides testing datasets from whole slide imaging for development of AI-based diagnostic assistance tools. Medical professionals evaluate results obtained by using developed tools to assist diagnostic procedures and tests.

Univerzitna Nemocnica Martin (University Hospital Martin)

The service will provide testing datasets from radiology for development of AI based diagnostic assistance tools. Medical professionals will evaluate results obtained by using developed tools to assist the diagnostic procedures and tests.

HUMANI SC - Hospital network (HUMANI)

Access to annotated and curated HUmani's patient datasets along with expert guidance in selecting relevant variables for specific use cases.

Politecnico Di Milano (POLIMI)

This service provides recommendations on how to implement the explainability models on the AI system of interest. The implementation can also be performed by POLIMI expert. Keyword: Explainaibility of AI algorithm

Politecnico Di Milano (POLIMI)

Generation of synthetic virtual cohorts leveraging statistical and data-driven methods and featuring realistic anatomies, flow fields, and boundary conditions. These virtual populations can serve multiple purposes, from in silico trials for testing new devices and procedures, to training large deep learning models for disease-specific tasks and discoveries. Method Description: The service provides synthetic datasets for AI algorithm development, AI algorithm testing and digital twin based simulations (fluidynamic/structural simulations, fluid structure interaction) Method reference: https://doi.org/10.1016/j.cmpb.2023.107468

Karolinska Institutet (KI)

## Overview Identifying and acquiring existing data sets—full-service data management. This service identifies data sets and makes them available to SME’s for TEF-project purposes, such as validation and testing of AI systems. Data can be collected from routine health records and local, regional, and national databases in collaboration with healthcare providers in Sweden. We provide expertise and technical support in the following areas: - Project design & management - Ethical application support - Data assembly - Data cleaning, annotation, anonymisation ### How can the service help you? Access to existing health data can help you validate your AI solution without needing to generate new data. We provide access to high quality datasets for validation and testing of your AI system to facilitate placing it on the market, and supporting to increase its market readiness level. Validation and testing data can be used for independent performance evaluation of your AI system in a real-world or simulated environment. ### 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 Data access and usage are facilitated through a secure environment managed by Karolinska Institutet. Datasets will be identified and made available to suit your AI system, to support with testing and validating your solution. The data will be tailored to your specific request to ensure it is compatible with your AI system, with full support from Karolinska Insitutet to ensure compliance with all relevant regulations. Upon completion of this service, you will receive an evaluation of the performance of your AI system, which can support the placing of your solution on the EU market. ### Service customization The service can be customised according to your specific needs, taking into account which type of data you require. --- ## Use case example ### Context A biotech SME is developing an AI-based system designed to improve cancer diagnosis, treatment planning, and prognosis prediction. To gain regulatory approval and to facilitate market entry, the SME requires access to diverse, high-quality datasets, including cancer imaging, electronic healthcare records (EHR), and multi-omics data, for independent performance evaluation. ### Objective The goal is to validate the AI system's ability to analyze complex datasets and deliver accurate, reliable outputs in simulated and real-world scenarios. ### Solution The identification and enablement of existing health data for AI solutions service, provided by the Swedish TEF-Health node in collaboration with Karolinska Institutet, offers ethically sourced datasets tailored to the SME’s needs. ### Implementation #### Ethical Agreement The SME enters into an ethical agreement with researchers from the Swedish TEF-Health node, ensuring all data collection and usage comply with GDPR and national Swedish regulations. #### Data Collection - Cancer Imaging Data: Retrospective datasets from imaging modalities like CT, MRI, and PET scans, annotated for various cancer types and stages. - EHR Data: Pseudonymised clinical data, including patient histories, treatment outcomes, and longitudinal follow-up records. - Omics Data: Retrospective genomic, transcriptomic, and proteomic datasets linked to cancer cases. #### 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. #### Validation and Testing Through a collaboration between Karolinska Institutet and the SME, the dataset is used to test the AI system's ability to: - Detect cancer accurately across imaging modalities. - Predict treatment responses and outcomes using combined EHR and omics data. - Identify biomarkers associated with different cancer subtypes and prognostic outcomes. ### Outcome Following the validation and testing, the AI system can be shown for its robustness, accuracy, and reliability, supporting regulatory approval and enhancing confidence for healthcare providers and stakeholders. ### Benefits - **Comprehensive Dataset**: Integration of imaging, EHR, and omics data ensures the AI system is tested across real-world complexities. - **Ethical and Secure**: Compliance with ethical guidelines builds trust and supports regulatory approval. - **Accelerated Innovation**: Access to retrospective data saves time, allowing the SME to focus on model optimization and deployment. ### Impact The validated AI system enhances early cancer detection and personalized treatment planning, improving patient outcomes while streamlining healthcare workflows.

Centre D'Excellence En Technologies De L'Information Et De La Communication (CETIC)

We specialize in developing and evaluating medical image analysis models using deep learning algorithms for tasks such as binary classification and anomaly localization. Our AI models are rigorously tested and validated in real-world hospital settings through prospective studies, ensuring their accuracy and reliability. How can the service help you? We aim to improve the clinical relevance and effectiveness of our AI models by leveraging our experience of real-world datasets achieved through close collaboration with healthcare professionals. This hands-on approach allows us to develop and evaluate AI models in real-world contexts, ensuring that they meet the specific needs of healthcare applications and work reliably in clinical environments. How will the service be delivered? The service will be delivered in several steps: - Consultation & Needs Assessment : Understanding your specific requirements and use cases. - Model Development & Optimization : Designing and optimizing AI models for medical imaging tasks. - Testing & Validation : Testing the models with real-world datasets to ensure performance and reliability. - Pretrained Pipeline Delivery : Providing custom pretrained pipelines for your needs. - Ongoing Support : Offering continuous support to refine and improve the models based on feedback.

Universita Degli Studi Di Pavia (UNIPV)

This service offers the construction and simulation of neurons and micro-circuits for neurological and neuropharmacological applications. The models can be combined to develop and simulate brain digital twins of neuropathologies. Method description: Customization of micro-circuits tailored to specific pathological and/or pharmacological needs. These circuits will be refined and optimized for simulations, with the resulting data integrated into a clinical framework and/or used to model pathological behaviors in a virtual brain twin. Method reference: Local method respecting General Data Protection Regulation (GDPR).

Univerzita Komenskeho V Bratislave (UK BA)

This service provides evaluation of the performance of AI algorithms, whether applied to supervised or unsupervised learning tasks. The evaluation encompasses accuracy, reliability, robustness, and calibration, utilizing standard performance metrics, task-specific metrics, SME-supplied data, and relevant publicly available datasets. Furthermore, the assessment includes an analysis of the AI algorithm's performance across various subpopulations.

Centre D'Excellence En Technologies De L'Information Et De La Communication (CETIC)

Support in setting up a privacy-preserving architecture (Secure Platform for Medical Data Analysis) on premise (local private cloud) when anonymisation/ pseudonymisation cannot be guaranteed due to the dataset or processing characteristics. Supports various privacy preserving techniques (federated learning, split learning, …) and especially for multicentric approaches. Method Description: Support to install a secure architecture for multicentric analysis of medical data with a complete federated model execution environment if needed. Depending of the use case, we can propose: * Design of cloud architecture * Design of federated learning architecture * Aggregator optimisation in federated learning * Enhanced security : to secure the model shared between the partner in the coalition. ** Full Homomorphic Encryption (FHE) : perform operations on encrypted data without having to decrypt it. ** Secure Multi-party Computation (SMPC) : enables several parties to work together to perform calculations on shared data without revealing the underlying information. ** Differential Privacy (DP) : Addition of Guissian noise in data to reinforce the protection of privacy. Method reference: https://www.mdpi.com/1424-8220/22/2/450 https://www.sciencedirect.com/science/article/abs/pii/S0020025518308338 https://www.intechopen.com/chapters/45421 https://ercim-news.ercim.eu/en126/special/inah-the-ethical-secure-platform-for-medical-data-analysis

Karolinska Institutet (KI)

The Swedish node is a collaboration between Karolinska Institutet, SciLifeLab and RISE. 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. Prospective data set collection: A full-service collection of new data sets. This service serves to create data sets on-demand according to the needs and requirements of the SME for TEF-project purposes, such as validation. Data sets will primarily comprise in vivo imaging data (PET, CT, MRI, MEG, EEG), ex vivo imaging, and OMICS data within neuro and cancer. Data can be generated by CIR and SciLifeLab infrastructures within our “Physical Testing” services or assembled from publicly available databases and research collaborations. We provide expertise and technical support in the following areas: o Project design& management o Ethical application support o Data collection o Data assembly o Data cleaning, annotation, anonymisation This service can typically directly collaborate with “physical testing services” and technology infrastructures to generate raw data material.

HUMANI SC - Hospital network (HUMANI)

Provision of clinical datasets that are curated, annotated by clinical experts, and available under several standardised formats upon request (OMOP CDM, FHIR, etc.). This service provided by clinical and IT experts, is aimed at delivering high-quality labelled real-world datasets to ensure efficient and high-performing AI solutions trainings.

HUMANI SC - Hospital network (HUMANI)

Provision of structured patient datasets organized in cohorts and mapped to international standards upon request (OMOP CDM or FHIR). This service ensures technical interoperability and facilitates multicenter databases validation by providing ready-to-use data for large-scale clinical evaluation.

Karolinska Institutet (KI)

## Overview This service provides a complete development of a Retrieval Augmented Generation (RAG) model for your SME’s needs. RAG is a cutting-edge AI methodology that has been gaining traction in recent years. RAG models combine the capabilities of classical large language models (LLMs) with the ability to retrieve relevant information from external knowledge sources, such as databases or document collections. This approach allows RAG models to provide more accurate and context-specific responses, stating the precise source of the information they generate. RAG models can be more resource-effective by focusing on relevant information retrieval rather than relying solely on extensive pre-training. RAG models have a wide range of potential applications, from specialized clinical tools, such as those used in molecular tumor boards, to more general applications, like serving as powerful search engines when connected to cloud storage. We provide expertise and technical support in the following areas: - Project design & management - Legal & ethical support SciLifeLab Data Centre has extensive experience in AI development and offers this comprehensive RAG service. Our service covers the entire AI lifecycle, including: #### Design - Understanding the problem - Requirements engineering - Data fitting #### Development - Modeling - Evaluation - Fine-tuning and optimization #### Deployment - Moving to production Please note that long-term maintenance and hosting responsibilities are not included in this service and will need to be managed by the SME. However, our team can provide preparation and advisory support before the service is finalized. ### How can the service help you? This service helps SMEs use advanced AI technology by creating Retrieval Augmented Generation (RAG) models designed to meet their specific needs. With expert support through every step of the process, the service ensures the model is effective, easy to use, and ready to deploy, helping SMEs make better decisions, save time and build confidence in their AI tools. ### 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. --- ## 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 develops RAG models, leveraging advanced AI frameworks and retrieval systems. Model development involves fine-tuning LLMs using frameworks such as PyTorch and TensorFlow and integrating them with external knowledge sources, such as databases or document collections, using retrieval mechanisms like ElasticSearch. Data preprocessing includes tokenization, vectorization, and indexing to ensure efficient information retrieval. The models are optimized for accuracy, relevance, and transparency, with quality assurance protocols applied to validate performance and compliance with ethical AI guidelines. All operations adhere to established standards for AI development and incorporate best practices for interoperability and scalability. ### 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 life science SME specializing in clinical decision support tools aims to develop an AI-driven application to assist molecular tumor boards in identifying personalized treatment options for cancer patients. The SME has access to large document collections, including scientific literature, clinical guidelines and patient case studies. However, the SME lacks the technical expertise to build a system that can retrieve relevant, context-specific information from these sources while integrating it with a large language model for natural language responses. This limitation hinders their ability to create an efficient tool for supporting oncologists, slowing product development and reducing stakeholder interest. ### Objective To develop a RAG model tailored to the SME’s needs, enabling seamless integration of large language models with their document collections. Ensure the model provides accurate, context-specific responses with transparent source references, supporting oncologists in making personalized treatment decisions. Deliver a fully optimized, production-ready model to enhance the SME's product development and scalability. ### Solution The SME collaborates with the Swedish TEF-Health node to develop a RAG model using frameworks like TensorFlow. Document collections are indexed with FAISS for efficient retrieval, and a pre-trained large language model is fine-tuned for clinical applications. The solution integrates scientific literature and clinical guidelines into the retrieval system, ensuring context-specific outputs. The final model is validated with TensorBoard for accuracy and relevance, with clear source references, and is prepared for deployment using Docker for scalability and secure handling. ### Implementation #### Ethical Agreement The SME enters into an ethical agreement with researchers from the Swedish TEF-Health node, ensuring that all data handling and model development comply with GDPR and Swedish national regulations, particularly regarding the use of sensitive clinical and scientific data. #### Data Preparation and Knowledge Integration The SME’s document collections, including clinical guidelines and scientific literature, are preprocessed for integration with the RAG model. This includes text cleaning, tokenization, and embedding generation using Hugging Face Transformers. The documents are indexed with FAISS to enable efficient and accurate information retrieval. #### Model Development and Fine-Tuning A pre-trained large language model is fine-tuned on the SME’s specific domain data using TensorFlow. The retrieval system is connected to the language model to ensure context-specific outputs. Recall@K, BLEU, and precision are used as metrics to validate the model’s accuracy and relevance during development. #### Validation and Optimization The integrated RAG model is validated using the evaluation frameworks TensorBoard, enabling real-time tracking of performance metrics and optimization of hyperparameters. This ensures the model is robust. #### Deployment Preparation The final RAG model is containerized using Docker to ensure scalability and ease of deployment. Advisory support is provided to the SME, including documentation and best practices for hosting and maintaining the model post-deployment. ### Benefits - **Insight Optimization:** Enhancing SMEs’ ability to deliver accurate, context-specific insights for molecular tumor boards. - **Credibility:** Fine-tuned and validated models build trust with stakeholders, including clinicians and regulators. - **Transparency & Compliance:** Clear source references foster trust and support ethical and legal standards. ### Impact The SME’s RAG model establishes itself as a reliable and cutting-edge tool in clinical decision support, gaining recognition within the oncology and AI research communities. The model’s ability to deliver accurate, context-aware insights with transparent references enhances trust among clinicians and regulatory bodies. This innovation fosters new collaborations with academic researchers and industry stakeholders, accelerating the adoption of AI-driven personalized treatment solutions and positioning the SME as a leader in advanced healthcare technologies.

Friedrich-Alexander-Universitaet Erlangen-Nuernberg (FAU)

Providing expertise and recommendations on analysing biases in the dataset or between datasets that could influence the performance of the algorithm. Differences could, for example, be caused by measurement devices or the operator. Method Description: For unknown biases can be tested with, for example, clustering algorithms or layer-wise relevance propagation for supervised learning. Method reference:

Friedrich-Alexander-Universitaet Erlangen-Nuernberg (FAU)

Providing expertise and recommendations on the handling and standardization of data. Method Description: Individual recommendations for data storage and retrival, considerations regarding usage of datasets in ML Method reference:

Friedrich-Alexander-Universitaet Erlangen-Nuernberg (FAU)

Providing expertise and recommendations on how to test and how to evaluate AI systems with respect to explainability, robustness, and performance. Method Description: Method reference: e.g. https://doi.org/10.1007/s10489-023-04532-5

RISE Research Institutes Of Sweden

RISE Cyber Range is a secure and realistic training and testing environment. The service also includes controlled penetration testing. Organizations can use the environment to practice and evaluate their ability to prevent, detect, and manage cyberattacks. The service is designed to be understandable even for non-technical decision-makers. It provides a clear picture of an organization’s cybersecurity maturity. RISE Cyber Range uses highly realistic IT and OT environments. These environments are used to identify technical vulnerabilities through penetration testing. They are also used to improve incident response capabilities. The service strengthens collaboration between technical teams, management, and business functions. How can the service help you? The service helps organizations to: - Perform penetration testing - Train staff using realistic cyber incident scenarios - Test technical safeguards and processes in a safe environment - Evaluate organizational readiness and decision-making under pressure - Identify improvement areas before real incidents occur How the service will be delivered The Cyber Range environment is provided by RISE and can be delivered on-site, remotely, or as a hybrid solution depending on customer needs. Delivery is planned in close cooperation with the customer and can be conducted as a single engagement or as a series of recurring exercises over time. The duration of the exercises ranges from a few hours to several days, depending on the scenario and desired level of depth. The customer is expected to provide high-level information about their organization, relevant threat scenarios, and participants for the exercise. RISE delivers a fully configured cyber range environment, scenario-driven attacks, facilitation, and technical and methodological support throughout the engagement. After completion of the service, the customer receives structured feedback in the form of observations, analysis, and recommendations. Service customization The service can be customized to meet the customer's specific needs, technical environment and level of maturity in cybersecurity. The assessment process begins with a joint meeting where the customer and a technical team from RISE discuss different options and tailor a service that is designed based on the customer's needs.

Centre D'Excellence En Technologies De L'Information Et De La Communication (CETIC)

Hospital processes optinisation. CETIC offers its expertise in the field of data management, process simulation, and other innovative expertise that could best optimize the multiple flows related to processes in a hospital, such as the sterilization process. The idea is to build a simulation model of the centralized shared sterilization process based on existing data from several hospitals in order to test and optimize the sizing of the future process. How can the service help you? This service provides a simulation service of sterilization facilities, to evaluate the sizing and robustness of such facilities against the expected flow of trays to sterilize. How the service will be delivered? The service will be delivered by first collecting relevant input data, which includes the medical tool documentation (detailing the description of medical tool sets exported from the sterilization software) and activity data (providing traceability of the passage of sets within the sterilization department). Additionally, data representing the sterilization facility and its environment, such as transportation logistics (e.g., by trucks if applicable), will be gathered. Once the input data is collected, a series of scenarios will be simulated to assess the performance and capacity of the sterilization facility. These simulations will validate investment decisions by providing key performance indicators, including the occupancy rate and overall performance of the facility under various operating conditions. Method reference: Discrete event simulation

Technische Universitaet Muenchen (TUM)

In silico training and optimization: the customer will specify an architecture for software controllers for robotic systems, which will then be deployed in concurrent simulations for e.g., training through reinforcement learning or optimization through genetic algorithms. Performance evaluation of AI-based controllers for robotics based on virtual testing environment: the system provided by the customer, for instance a robotic device using an AI module in closed loop with sensors and actuators for sensorimotor control purposes, will be tested using a simulated environment with sufficient physical realism. Method Description: The Neurorobotics Platform (NRP) is a simulation framework for implementation of highly modular, physically realistic simulations, with a focus on robotics. It also supports the execution of such simulations in the context of an online service on EBRAINS, and can be used in conjunction with standard RL or optimization frameworks. It enables the design, functional evaluation and (at the end of TEF-Health) certification activities for robotic systems, including those with AI modules embedded. Method reference: To be refined depending on the type of task. Either ISO references, scientific literature, work done by WP7, or metrics designed by the TEF-Health consortium.

Univerzitna Nemocnica Martin (University Hospital Martin)

Provisioning of clinical data from diagnostic procedure of sleep disorders within sleep laboratory and consultancy related to data processing and development of AI diagnostic asistance tools Method Description: Sleep disorder diagnostics require to record various types of data during the whole diagnostic procedure. Video, audio, ECG, EEG and other diagnostic data are being recorded during the sleep phase of a patient in the sleep laboratory. Following evaluation procedure is time consuming for a medical professional. AI based data processing and decision support system would greatly help to speed up the diagnostic procedure. UHM will provide data sets for development and testing of AI based applications which would help medical professionals within the sleep labs. Results will be evaluated by medical professionals. Method reference:

Friedrich-Alexander-Universitaet Erlangen-Nuernberg (FAU)

Providing an Open Source software framework to analyze IMU data for human gait and movement analysis Method Description: Python library for extraction of Spatial-Temporal gait parameters from raw IMU data Method reference: https://github.com/mad-lab-fau/gaitmap

Friedrich-Alexander-Universitaet Erlangen-Nuernberg (FAU)

Providing an Open Source software framework to simplify the comparison of ML and traditional algorithms Method Description: A generic way to build object-oriented datasets and algorithm pipelines and tools to evaluate them. Method reference: https://github.com/mad-lab-fau/tpcp

Politecnico Di Milano (POLIMI)

This service assesses algorithm performance, development progress, and adherence to AI Act regulations for implementation in clinical practice. The AI validation includes several aspects, aligned with TEF guidelines, and EU regulation: 1) Bias evaluation 2) Accuracy evaluation 3) Trustworthiness evaluation 4) Uncertainty evaluation. The validation will be offered on three levels, based on the source of the validation dataset: 1) End-user provides the data for validation (input features) 2) End-user provides data for validation at the start from raw data of the initial dataset (data curation and feature extraction on the validation set will be extracted by the Lab within the process of validation) 3) The POLIMI Lab provides a tailored external validation set acquired from other clinical centers. Keywords: Validation; Bias; Accuracy, Trustworthiness, Uncertainty, AI algorighm AI validation

Centre Hospitalier Universitaire De Grenoble (CHUGA)

Using an AI algorithm to produce virtual anonymized datasets imitating real data. This could help companies to easily access to datasets thanks to anonymization. Method Description: Depending on the SME needs and available ressources, this service may include all or part of the following task list: - generation of anonymous virtual datasets - qualification of these datsets as anonymous data Method reference: TBD

Centro Hospitalar De Sao Joao Epe (CHSJ)

The service offers SMEs access to hospital workflows and infrastructure within a controlled, virtual environment. By simulatin a hospital setting, it assesses de the compatibility, interoperability and reliability of systems before deployment in real-world scenarios, reducing risk, saves resources and streamlines the integration proccess by identifying and resolving issues early in the development lifecycle.

Centre D'Excellence En Technologies De L'Information Et De La Communication (CETIC)

To enhance the security of software-based products, ensure compliance, and mitigate risks, CETIC offers expertise in scanning your system with automated tools to identify known vulnerabilities and potential weak points that attackers could exploit. This method involves the use of specialized software to identify security flaws, such as obsolete software versions, incorrect configurations or unsecured open ports. Once these vulnerabilities have been identified, an action plan can be drawn up, including security measures to reduce the risks. We use industry standards such as OWASP Top 10 to configure the scanning process. How can the service help you? We will provide complete scan reports as well as recommendations to lower the residual risk level and attack surface of your system. Together with a risk analysis, they can be used as a complete set of evidences towards authorities and customers. How the service will be delivered? Our team will carry out comprehensive vulnerability analysis for your software product, and help you define the most appropriate action plan, taking into account your specific requirements and context. We scan your system using automated tools to detect known vulnerabilities or weak points that could be exploited by attackers. This method involves the use of specialized software to identify security flaws, such as obsolete software versions, incorrect configurations or unsecured open ports. Once these vulnerabilities have been identified, we define with you actionable recommendations including security measures to reduce the risks. Optionally, we can complement with an analysis of the source code by a tool that will scan the whole codebase searching for security violations. This will further improve the cyber-resilience of your product. Optionally, we can perform the security risk analysis of your product. Service deployment: The service is usually "deployed" by simply having remote access to your product, so as to execute the vulnerability scanning. For the optional source code analysis, we need access to the code base. Resources provided to client: Cybersecurity tests report Recommendations Method reference: OWASP Top 10