Large Language Models (LLMs) are transforming healthcare by removing static data entry and manual workflows. Yet, slow training cycles, costly integrations, and brittle AI logic hinder adoption.
LLMs act as a bridge between humans and machines, often in the form of chatbots. However, these systems are plagued by errors, frustrating loops, and outdated training models that take months—or even years—to improve. Meanwhile, Agentic AI, which executes tasks based on system events, struggles with a lack of corrective feedback and rigid interfaces.
These limitations erode trust and prevent AI from reaching its full potential in healthcare. What if AI could continuously learn provider preferences, adapt to local policies, and refine patient interactions in real time? This would create a system that not only responds correctly but also improves dynamically—enhancing accuracy, efficiency, and safety over time.
Join us to explore a novel approach to Healthcare AI efficacy, where we train open-source LLMs, develop self-correcting agents, and integrate real-time FHIR data to bridge the gap between technology and patient care.
In this webinar, we will cover:
- Training Open-Source LLMs for Healthcare – Overcoming long training cycles and costly model updates
- Building Self-Correcting AI Agents – Creating AI systems that learn from user interactions and refine their responses dynamically
- Enhancing Clinical Workflows with Real - Time FHIR Data – Streamlining data exchange and reducing manual entry
- Addressing AI Trust & Reliability Challenges – Tackling errors, feedback loops, and brittle interfaces in AI-driven healthcare
- Practical Demonstrations & Use Cases – Showcasing real-world applications of AI-driven automation in clinical settings
Live event date: Tuesday, April 29, 2025 | 12 p.m. ET
On-demand event: Available for one year afterward.
Ben Cushing
Chief Architect, Health and Life Sciences, Red Hat
Sam Schifman
Innovation Architect, Vantiq
Wes Jackson
Senior Solution Architect, Red Hat
