Plan goals, priority use cases (administration vs. clinic), governance model (accountability, auditability), rules for pilots and scaling, data requirements and integrations, and metrics for benefits and risks. Practically: what do Ministry of Health of Slovak Republic and hospitals need to do in 2026 to be able to deploy AI safely and meaningfully?

Practical deployment of AI in medical diagnostics, focusing on the difference between clinically mature solutions and areas with significant operational and regulatory risks. Emphasis is placed on the real-world benefits of AI, validation, and post-deployment monitoring, integration into PACS/NIS systems, and liability issues. The practical part focuses on selecting the appropriate use case and on setting up quality control to achieve long-term value in clinical practice.

Rules for the safe use of AI for patients and HCPs. What information can be safely shared, what are the communication limits and what are the risks? 

What does a hospital realistically need to have (patient identity, coding, documentation structuring, data quality, auditability of accesses, sharing rules) to be compatible with the European direction? Emphasis on pragmatic approach: how to prepare without complete "rip & replace" systems.

Today, the sustainability of healthcare cannot be judged separately from its quality. The stability of the system depends on the availability and professionalism of health professionals, adequate financing arrangements, and the ability to respond to the growing needs of patients. The panel will focus on how to build quality and long-term sustainable healthcare that is staff manageable, financially realistic and patient-centred. The discussion will raise the question of what really constitutes quality in healthcare today and how to realistically measure it. How to set up a quality measurement system for hospitals and how to make it work?