This talk presents a practical case of introducing AI-based quality control in a medical diagnostics lab, where the implementation led to measurable accuracy improvements and reduced manual work.

We’ll cover:

1. The overall strategy for AI implementation;
2. Data governance and compliance challenges (EU MDR, GDPR);
3. Key architectural and technology choices—including LLMs to handle data from a constantly evolving ecosystem of medical equipment, phased learning loops, privacy-by-design, and seamless integration into existing workflows.

Target Audience: Decision Makers, Software architects, Strategists, AI practitioners, QA engineers
Prerequisites:General awareness of AI or data-driven systems
Level: Introductory