Agenda

Discussion Question

How do we validate AI in healthcare in a way that simultaneously:

1. Demonstrates measurable clinical efficacy against real-world patient outcomes,
2. Ensures reliability, safety, and contextual appropriateness of large language models and AI systems within the environments in which they are deployed, and
3. Prevents the amplification of health disparities, ensuring these technologies improve outcomes across diverse and underrepresented populations?

Discussion Question

What does the journey from regulatory clearance to recurring revenue for AI products actually look like? Specifically:

1. Which CPT codes, billing modalities, and reimbursement frameworks currently offer the strongest revenue potential for AI-enabled clinical tools?
2. What payer contracting and enterprise sales strategies give an AI company the fastest path to scalable, predictable revenue?
3. What specific steps must a company execute after FDA clearance to bridge the gap between regulatory approval and actual market adoption?

Discussion Question

How do we build a regulatory framework that keeps pace with the speed of AI innovation? Specifically:

1. How should regulators continue to evolve approval pathways to accommodate adaptive, self-improving AI systems and rapidly evolving diagnostic algorithms?
2. What frameworks are needed to regulate the explosion of AI-enabled wearable devices and personalized diagnostics entering the market at unprecedented speed?
3. How do we strike the right balance between enabling innovation velocity and maintaining rigorous safety standards that protect patients across diverse clinical settings?

Discussion Question

Where is AI creating the most measurable operational value inside health systems today? Specifically:

1. In which operational areas is AI delivering the most quantifiable return on value right now— workflow automation, clinical documentation, readmission prevention, or resource allocation?
2. How are health systems measuring and attributing real-world impact such as time saved, clinician burden reduced, and costs avoided in a way that justifies continued AI investment?
3. How do we ensure that AI-driven value creation reaches underserved and diverse patient populations equitably — rather than concentrating gains in well-resourced health systems and widening existing disparities in care?