Program
Founder and General Manager of the Belgian e-Health Platform, CEO of Smals, Belgium
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.
Technical Physician, The Netherlands Cancer Institute, NKI, Netherlands
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
Chief Information Officer at General Hospital of Granollers/Barcelona, Spain
When you realize that we are witnessing a revolution rather than just a new technological advancement, you have an obligation to do everything in your power to integrate it into your organization.
This presentation will outline the roadmap for incorporating Generative Artificial Intelligence at Hospital General de Granollers. It will also include real-world use cases currently being developed and implemented. Attendees will gain insights into the strategic approach taken by the hospital, the challenges faced, and the transformative impact of this cutting-edge technology on healthcare practices.
Chief Innovation Officer at MyData-TRUST, 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
More and more, people start to appreciate the importance and value of data. This is not only accelerated by the ongoing growth and adoption of AI, but also because it is one of the cornerstones of the EU data-strategy. More specifically, the EU data-strategy also encourages to increase the potential of data by supporting data sharing approaches using ‘data spaces’. In his talk Hans will high-light what data spaces are and how they relate to working with health-related data in the EU. He will use a number of examples from his own experience to describe the current state-of-practice and identify challenges and opportunities going forward.
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
Innovation Director at Bahia Software, Spain
Developing AI systems for use in medical settings is a complex, multi-stage process. Relatively few AI developers publish the methodology of such developments. While healthcare organisations and physicians see many approved devices without sufficient evidence, startups and companies developing AI systems are demanding technical, operational and ethical standards. Meanwhile, regulators such as the US Food and Drug Administration (FDA) or the European Medicines Agency (EMA) have approved hundreds of AI-powered systems for use in hospitals and clinics through less rigorous processes than those for drugs.
It has become clear to policymakers, regulators, clinicians, patients and the scientific and technological community that AI standardisation should be implemented to ensure patient rights, fundamental values and the desired impact on healthcare organisations. The presentation aims to share the CHAIMELEON’s findings in this specific topic. A preliminary toolkit with guidelines related to the design, development and validation of AI systems in healthcare will be shared with the audience. Finally, some concrete examples of AI models in the health domain will be presented.
Scientific Director Data Governance at Sciensano and Professor in Smart Statistics for Policy Design and Development at Maastricht University, Belgium
Advisor at the World Health Organization (WHO), France