Improving Clinical AI Accuracy, Efficiency, and Cost Through Semantic Grounding

As healthcare organizations expand their use of AI, balancing clinical accuracy, reliability, and cost is becoming increasingly challenging. While large language models offer significant potential for clinical applications, rising inference costs and the need for trustworthy, explainable outputs present important considerations.

Join us to explore how semantic grounding, clinically validated knowledge graphs, and emerging AI architectures can address these challenges. Through real-world use cases and performance insights, discover approaches to optimizing AI workflows, reducing computational demands, and supporting more informed clinical decision-making.

You’ll learn how to:

• Improve clinical AI accuracy and cost efficiency through semantic grounding and more targeted use of language models
• Examine how knowledge graphs, deterministic workflows, and agentic AI can strengthen clinical AI applications.
• Evaluate the impact of grounded AI on output quality, explainability, and inference costs