Topic #2
Co-Intelligence: What Actually Works When AI Meets Medicine
In an era of AI hype, this keynote delivers what health and care leaders desperately need: evidence. Real deployment action. Honest limitations. Transferable principles. Whether you're evaluating your first AI pilot or trying to understand why your third one failed, you'll leave with a battle-tested framework for co-intelligence that actually empowers doctors instead of overwhelming them. No revolution promises.
Most AI in healthcare fails quietly. Expensive tools nobody uses. Pilots that never scale. Accuracy numbers that mean nothing in practice.
Occasionally it works. When it does, there are patterns.
This keynote distills what separates AI success from expensive failure across clinical domains. Real deployment data. Honest limitations. Transferable principles.
Here's what nobody tells you: infrastructure determines outcomes more than algorithms. The best AI model fails without rapid-action capacity. The mediocre one succeeds when the system is redesigned around it.
Geography beats technology. Workflow redesign beats features. Human-AI collaboration beats autonomous systems. Every time.
Key Learning Outcomes:
- Why high accuracy doesn't equal clinical value.
- The 30/70 rule.
- How to prevent both overload and misses.
- When AI should triage, not diagnose.
- Infrastructure prerequisites nobody mentions.
- Safety monitoring from day one.
- The red flags that predict failure.
- The honest questions to ask before signing contracts.
Examples span radiology, oncology screening, emergency medicine, and beyond. One case reduced diagnostic wait times by 40% and increased same-day interventions 6-fold. Another improved cancer detection by 20% while freeing specialist capacity by 30%. Both succeeded for the same reasons - and failed at other sites for predictable ones.
You'll see what the evidence shows and what remains unknown. Process metrics vs outcome data. Single-center results vs transferability. Technical accuracy vs clinical utility.
This isn't about AI replacing doctors. It's about empowering clinical judgment with tools that actually work.
Whether you're evaluating vendors, planning deployments, or figuring out why your current AI isn't being used - you'll leave with a framework built from what survives contact with clinical reality.