Program
Director of Trusted AI Labs (TRAIL), UCLouvain, Belgium
Continual active learning is a transformative approach in the enhancement of screening techniques, particularly for breast cancer, colon cancer, and certain neurological diseases. This method integrates continuous learning and active data collection, allowing artificial intelligence (AI) systems to progressively improve their accuracy and efficiency. Drawing inspiration from the MyPeBS (My Personal Breast Screening) project, which focuses on personalized breast cancer screening across Europe, continual active learning involves AI systems that actively query experts in uncertain cases, learning from these interactions to refine their predictive models over time. This iterative process not only enhances the AI’s diagnostic capabilities but also ensures that it adapts to new data and evolving medical knowledge, leading to more accurate and timely detection of diseases. By leveraging continual active learning, healthcare providers can significantly improve the effectiveness of screening programs, ultimately leading to earlier detection and better patient outcomes.
Partner at Syte, Germany
• Success factors for an increased AI implementation in healthcare
• Description of a large scale real world AI in Diabetes and Obesity usecase with >150.000 patients
• Benchmarking between AI and physician decision making
• AI driven efficiency increase for GLP 1 Receptor Agonists
CEO, Opal Solutions, Belgium
This talk explores how generative AI can be harnessed to meet the specific needs of data protection in the life sciences sector. Instead of focusing on AI development, we will dive into three practical use cases that highlight MyData-TRUST’s enhanced efficiency through AI-driven solutions:
- Regulatory Queries: Streamlining responses to complex regulatory inquiries.
- Document Queries: Enhancing search capabilities within extensive document repositories.
- Document Compliance Checks: Automating compliance assessments to ensure adherence to regulatory standards.
These examples illustrate how generative AI can revolutionize data protection practices, making processes faster, more accurate, and compliant with industry regulations.
Medical Physicist-Clinical Data Scientist, Postdoc at MAASTRO clinic-Maastricht University, Netherlands
In my presentation I will explore the critical role of AI in transforming the landscape of health data management across Europe. Drawing from my extensive experience as a researcher in AI and FAIR data infrastructures, as well as my involvement in various international research projects, I will present how AI-driven solutions can revolutionize the curation, integration, and reuse of personal health data. This presentation will highlight the challenges of fragmented health data, the potential of AI to automate data curation processes, and the importance of creating interoperable and patient-centric health records. By showcasing real-world examples and use cases, I aim to demonstrate how these advancements can empower patients and enhance clinical research.
Chairman of the Board of Yuma, Netherlands
Partner, Hogan Lovells, Belgium
The purpose of the presentation will be to give an overview of the regulatory requirements and challenges which apply to manufacturers of AI-based medical devices in the EU. The presentation will start with a discussion of current key considerations for the CE marking of these devices under the EU MDR and IVDR before analysing the impact and challenges posed by the AI Act on future conformity assessments; The presentation will also include some considerations from a healthcare professional perspective on the use of AI-based medical devices in daily practices.
Partner at Syte, Germany
• The EU and US AI Act
• Differences in their implementation for AI in healthcare
• Potential pathways to measure medical and economic impact of AI in healthcare
• Ethical Guidelines in AI-driven healthcare decision-making
Advisor at the World Health Organization (WHO), France