Quality and Trust in GenAI

Build Gen AI applications that healthcare can trust

The adoption of generative AI in healthcare is accelerating, but innovation alone isn’t enough. What sets successful organizations apart is their ability to build Gen AI solutions that are not only powerful but also trustworthy, safe, and clinically sound.

Whether you're deploying AI-powered clinical assistants, automated documentation tools, or decision support systems, ensuring transparency, compliance, and reliability is essential to scale Gen AI responsibly. This paper offers real-world practices and guiding principles to help AI, engineering, and data science teams design with confidence and deploy with impact.

The paper outlines a practical framework to develop GenAI applications that align with healthcare’s rigorous standards. It delves into the critical pillars of trust:

• Data quality
• Model governance
• Explainability
• Continuous validation

For a reliable and scalable solution, these pillars must be embedded from the ground up. Learn how leading healthcare enterprises are addressing key risks like hallucinations, bias, and lack of traceability through robust architecture and lifecycle design. Download the full perspective.