This symposium explores how advances in artificial intelligence, real-world evidence, and co-design are reshaping how medicines are evaluated and how decisions are made in healthcare. As health data becomes increasingly complex and distributed across multiple sources, there is a growing need for approaches that support integration, interpretation, and meaningful translation into practice and policy.

The session brings together three complementary angles to addressing these challenges:
• Computational approaches: How open-source Large Language Models (LLMs) can transform noisy reporting systems into structured, explainable insights without relying on proprietary software.
• Real-world evidence and decision-making: Insights from the More-EUROPA project on integrating registry-based real-world data into regulatory and Health Technology Assessment (HTA) decision-making in an ethical and patient-centred way.
• Collaborative practice: Reflecting on the practical "spirit" of co-design, addressing the challenges of multidisciplinary collaboration between designers, clinicians, and software developers.

Together, these presentations illustrate how artificial intelligence, real-world evidence, and participatory design can better connect evidence generation with decision-making, ultimately improving how medicines are evaluated and used in practice.