Download the White Paper: The AI Paradox in Healthcare

Healthcare organizations recognize the enormous potential of AI, but putting it into practice has proven challenging. Existing initiatives often get stuck at the stage of isolated experiments. The need is clear: less administrative burden, faster access to protocols, and better support for both professionals and patients. At the same time, concerns about privacy, incorrect answers (hallucinations), and strict laws and regulations (such as the GDPR, MDR, and the AI Act) are understandably significant.

In a sector where tolerance for errors is minimal, this creates a paradox: how do you deploy AI without compromising safety and compliance?

The solution lies not in the open internet, but in your own data. In this white paper from Dawn Technology, you’ll discover how  Retrieval Augmented Generation (RAG) combines generative AI with your own, validated knowledge sources.

What you’ll discover in this white paper:

• How RAG works in healthcare: Learn how AI generates reliable answers, including verifiable source references.
• 4 use cases with immediate impact: Discover the “low-hanging fruit,” including protocol and guideline assistants, internal IT support, MedTech documentation, and patient support.
• Custom vs. Off-the-Shelf: Learn why a custom RAG solution, integrated with existing systems such as EHRs and ECDs, delivers more value than a standard AI tool.
• A practical step-by-step plan: From selecting the right knowledge sources to setting up a secure, well-defined pilot.

“The greatest benefit of AI in healthcare lies not in yet another standalone tool, but in reliably making existing knowledge accessible. When you base answers on validated sources, with clear citations and human oversight, AI becomes truly practical in real-world applications.”

— Govert de Vries, Director Health & Lifestyle, Dawn Technology

Who is this white paper for?

This publication is essential for healthcare executives, innovation managers, IT professionals, and QA/RA specialists in the MedTech and healthcare sectors who are looking for verifiable and secure ways to reduce their workload using AI.