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.

  View service →
• 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

  View service →
• 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

  View service →
• 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

  View service →
• 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

  View service →
• AI readiness level and Machine learning feasibility investigation

  RISE Research Institutes Of Sweden

  Method description: Investigate Data to be usable in an AI context. The service is customized according to the SME's needs. Method reference: To be refined depending on the type of task, and in dialog with the customer. The Center for Applied AI at RISE carries out cutting-edge research in AI, connects expertise and applications within RISE, and explores a wide range of innovative applications with industry and the public sector. Applied AI Centre at RISE helps companies and government agencies to see more potential in the technology, use it more wisely and develop it faster. Keywords: Trustworthy AI, Federated learning, Data Science, AI, Deep Learning, Natural language processing, Computer Vision, Machine learning.

  View service →