AI-driven advances in computer graphics and vision enable detailed 3D modeling and simulation of the human body, yet remain underused in clinical practice, creating a widening gap increasingly recognized by venues like SIGGRAPH, Eurographics, and CVPR; this session presents holistic 3D patient modeling and predictive simulation approaches, showcasing clinical applications and outlining requirements for integration, from learning with limited data to physics-based, real-time simulation across adult and pediatric care.

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This track explores how advances in AI, particularly large language models, are reshaping education—from personalized learning and assessment to system-wide transformation. Bringing together academia, industry, and AI engineering, the session combines short talks and a panel to examine scalability, reliability, and current limitations of LLMs, as well as deployment challenges in real-world settings. It also addresses risks of over-reliance and the need for human-centered, trustworthy systems, concluding with a forward-looking discussion on adaptive learning and human–AI collaboration.

This track examines the growing concentration of power among dominant social media platforms and its impact on the global information environment, including risks such as disinformation and reduced diversity of perspectives. It highlights the need for integrated legal and technical responses, presenting recent efforts in Europe and Switzerland to address these challenges. Through talks, demos, and a panel, participants gain insights into current frameworks, their limitations, and emerging techno-legal solutions to mitigate systemic risks.

This track brings together cognitive scientists, AI researchers, artists, and philosophers to explore what “intelligence” means across disciplines and why it matters for AI design. It examines how psychological concepts have shaped AI, questions the focus on abstract reasoning over embodied and social cognition, and considers whether AI reflects human or fundamentally different forms of intelligence. Through short provocations and discussion, participants engage in cross-disciplinary dialogue aimed at designing AI that supports human flourishing.

This session explores how AI is transforming science and engineering, moving from specialized models to generalized, multi-modal foundation models that integrate data-driven and physics-based approaches. Covering applications from chemistry to neuroscience, it highlights advances such as generative AI and physical tokenization, while addressing challenges like data scarcity and model robustness. Through talks and a panel, participants gain insights into how these methods can accelerate innovation, reduce engineering costs, and support more sustainable design.

This track explores how AI and machine learning can advance natural hazard and risk management, addressing threats such as earthquakes and landslides. It highlights emerging methods, including foundation models in Earth sciences and agentic AI for impact simulation, with a focus on digital twins and data integration. Topics include learning from sparse, noisy data, handling uncertainty, and developing physics-informed models for prediction and early warning, alongside insights into European efforts to build trustworthy AI-driven hazard systems.

This track explores how AI can create real, scalable impact in healthcare and medicine development, bringing together researchers, clinicians, and industry leaders. Through examples in precision oncology, medical imaging, and decision support, it highlights how data-driven innovation moves from models to practice. Key topics include data usability, validation, trust, and regulatory readiness, as well as integrating AI into clinical workflows and drug development through cross-sector collaboration.

This track discusses advances in generative AI approaches for protein and small molecular design, ideally with speakers from academia and industry.