Related services

• Access to Compound Collection

  Karolinska Institutet (KI)

  The Unit: Chemical Biology Consortium Sweden (https://www.scilifelab.se/units/cbcs/) The infrastructure is part of Science for Life Laboratory (SciLifeLab) which is an academic collaboration between Swedish universities (including Karolinska Institutet) and a national research infrastructure with a focus on life science. Chemical Biology Consortium Sweden (CBCS) is a national research infrastructure offering an extensive portfolio of services - from computational and organic chemistry to phenotypic small molecule screens in cells and model organisms, cell painting and functional precision medicine. The mission of CBCS is to provide a state-of-the-art platform and expertise for the generation of high-quality bioactive chemical tools (small organic molecules) for applications within life science research in general and with the ultimate goal to explore complex biology. Our units comprise compound handling labs (hosting the SciLifeLab Compound Collection of ca 350,000 molecules), screening labs (microtiter format liquid handling robotics and a wide range of plate readers), and chemistry labs (equipment and instrumentation for organic chemistry synthesis and analysis). The service: Access to compound collection https://www.cbcs.se/the-compound-collection The Scilifelab Compound Collection is managed by the Compound Center at CBCS KI, and is based on donations of high-quality sets from the pharmaceutical industry (Biovitrum, Karobio, Orexo/Biolipox, KDev) and has been developed and expanded with compounds from various commercial compound vendors. The compounds can be readily available in assay-ready plates through acoustic nano-litre scale liquid dispensing or in 96-tube racks; compounds are stored at 10mM concentration in DMSO solution at - 20 °C under humidity control. We provide: o Management, storage, and processing of solids and liquids. o Compound delivery and interface with key customers. o Performance of instruments and automation that support these operations. o Analytical techniques used for quality assurance. o Sample informatics, including registration and compound quality data.

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• AI Evaluation

  Vlaamse Instelling Voor Technologisch Onderzoek N.V. (VITO)

  Evaluation of medical AI using real-world Data and Synthetic Data to create a strong technical AI evaluation report. Method: evaluation of medical AI products, taking into account the differences between subpopulations, sources of bias, metric selection, uncertainty intervals, among others. Keywords: AI performance evaluation, test report

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• AI Evaluation Training

  Vlaamse Instelling Voor Technologisch Onderzoek N.V. (VITO)

  Specialized training on how to thoroughly evaluate the performance of Medical AI models and create test reports for notified bodies. Medthod: Organisation of in-person and online training for Medical AI companies to enable them to create strong technical AI evaluation reports. Keywords: AI performance evaluation, test report, course

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• AI Monitoring

  Vlaamse Instelling Voor Technologisch Onderzoek N.V. (VITO)

  Semi-automated performance monitoring of the Medical AI deployed in the clinical practice, following PMS regulation. Making the distinction between deployments where good model performance can be guaranteed and where not. Consultancy services on automated model performance monitoring in the clinical practice, in compliance with Medical Device Regulation (MDR). Keywords: AI performance evaluation, performance monitoring, PMS, PMCF

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• AI Privacy Risk Testing and Compliance Support

  RISE Research Institutes Of Sweden

  Your AI models might be leaking personal data or trade secrets. GDPR and the AI Act expect you to check this — not hope for the best. We deliver reproducible and defensible testing, so your decisions — and your audits — rest on evidence instead of assumptions. Organisations that build or deploy AI models must meet strict privacy and cybersecurity obligations under GDPR, the EU AI Act, and sector‑specific rules. Models can unintentionally memorise and leak personal data through attacks such as membership inference, data reconstruction, and data extraction — creating real compliance and reputational risks. GDPR requires organisations to prevent downstream exposure of personal data and to honour the Right to be Forgotten, which in practice demands testing whether individual training data still leaves detectable traces inside a model. Beyond privacy, AI models may also reveal proprietary or confidential business information, turning leakage into an IP and cybersecurity risk, not just a data‑protection issue. The EU AI Act reinforces this by requiring testing under foreseeable misuse, including privacy‑relevant attacks. LeakPro provides structured, empirical stress‑testing of AI models to measure privacy and IP leakage, validate privacy‑enhancing technologies, and generate audit‑ready evidence for DPIAs, GDPR, and AI Act compliance. This service maps to the following articles within AI-act: • Article 9 Risk management system — Continuous lifecycle risk management to keep your AI compliant and under control. • Article 10 Data and Data Governance — Validate that your training data meets state-of-the-art privacy and security standards. • Article 15 Accuracy, robustness and cybersecurity — Proactively detect and mitigate confidentiality attacks before they become real risks. The audit reports constitute evidence that feeds into the technical documentation (Article 11 — Technical documentation, Annex IV). How can the service help you? • Detect leakage of personal data, sensitive attributes, or proprietary information. • Check data traceability to support GDPR Right‑to‑be‑Forgotten obligations. • Validate and tune synthetic data, federated learning, and differential privacy. • Provide quantitative leakage‑risk indicators for audits, DPIAs, and procurement. • Advise on privacy‑resilient AI design already at investment and planning. How the service will be delivered Logistics • Delivered via RISE secure environments or customer infrastructure. • Supports models across text, tabular, image, time‑series and graph modalities. • Option for iterative collaboration during model development or one‑off audits. Delivery period Engagements are planned in phases depending on the scope, starting with a general assessment (qualitative), followed by attack simulation (quantitative) and reporting. Duration • Short engagement: 3–6 weeks • Complex model audits or PET optimization: 2–4 months Customer requirement • Provide intended use-case, regulatory context, and risk appetite • Documentation of model development and system environment. • Optional: Access to model artefacts and training data for technical study. Deliveries • Tailored attack suite configuration • Execution of privacy and IP leakage attacks • PET tuning recommendations • Compliance‑aligned summary (reporting evidence for GDPR, AI Act, etc) Output • Technical leakage‑risk metrics • Training data traceability assessment (Right‑to‑be‑Forgotten) • Harm‑oriented explanation for DPIAs • Recommendations for mitigation and governance

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• AI Technological watch and guidance and AI Technical tests and Feasibility Study

  Multitel (MULTITEL)

  ### Overview This service helps SMEs explore the potential of AI technologies and assess their feasibility before committing to a development or major technology choice. Multitel can support the customer in clarifying the application need, technological question, requirements and constraints, and then identify relevant AI approaches. Depending on the customer’s needs, the service can include a technological watch of emerging AI methods, comparison of potential technical approaches, exploratory analyses and technical tests on available data. The objective is to provide evidence-based guidance on whether and how an AI-based approach should be pursued, while considering technical limitations, performance expectations, healthcare requirements and constraints associated with the intended application. ### How can the service help you? The service is intended for SMEs and technology developers that are considering the use of AI but need expert support to clarify their technological needs, identify relevant technologies and determine whether the envisaged approach is technically feasible. **Before the service**, the customer may have a healthcare need, an initial idea, available data or an existing technological concept, without necessarily having identified the most appropriate AI approach or clearly formulated the technical question to be addressed. Multitel supports the customer in translating this need into a clearer technological problem and defining the relevant requirements and constraints. Its expertise in machine learning and deep learning, computer vision, multimodal data analysis, predictive and sequential modelling, signal processing and Trustworthy AI can then be used to investigate relevant technological options and, where needed, perform exploratory technical tests to assess feasibility. **After the service**, the customer has a clearer definition of the technological problem and receives documented conclusions and recommendations on relevant AI approaches, their feasibility, identified limitations and possible next steps for development or validation. When included in the agreed scope, the service may also result in a first proof of concept (PoC) demonstrating the technical feasibility of a selected approach on the available data. ### How will the service be delivered? The service starts once the objectives, scope, available information or data and expected outcomes have been agreed with the customer during the quotation phase. Depending on the agreed scope, Multitel performs a targeted technological watch and/or feasibility study. The work may include a review of relevant AI approaches, analysis of the customer’s technical constraints and available data, comparison of candidate technologies, and exploratory technical tests when required to assess feasibility. Exploratory work can lead to a first Proof-of-Concept. Intermediate findings can be discussed with the customer when relevant. At the end of the service, Multitel presents the conclusions, identified limitations and recommendations for possible next steps. The main results are documented in a feasibility report. The service is carried out by Multitel’s experts and does not require the customer to be on site. Customer interactions can be organized remotely. The execution time depends on the scope of the technological investigation, the complexity of the use case and whether technical tests are required, and is defined during the quotation phase. ###Use cases/examples **Example 1 – Feasibility of AI for a new healthcare application** A healthcare SME has collected sensor and clinical data and is considering adding an AI component to its solution but does not know which modelling approach would be appropriate or whether the available data are sufficient. Multitel reviews the technological options and available data and performs exploratory tests on selected approaches. **Outcome**: the SME receives a feasibility report describing the most promising approaches, observed limitations, technical risks and recommendations for subsequent development. **Example 2 – Technological watch before selecting an AI approach** An SME plans a new AI-enabled medical application and needs to understand which recent AI technologies could address its use case. Multitel conducts a targeted technological watch, compares relevant approaches according to the customer’s requirements and identifies their respective advantages, limitations and maturity. **Outcome**: the SME receives documented guidance supporting its technology choice and a clearer roadmap for the next development or experimentation activities.

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