Summary
The session explores the challenges of scaling AI in healthcare, focusing on data silos, interoperability issues, and governance concerns. Participants emphasize the need for standardized data, tangible use cases, and regulatory frameworks to unlock AI's potential to improve patient outcomes and transform healthcare delivery.
Key Topics
4 key topics from this session at Future Health Summit (FHS). Thicker branches were mentioned more often in the talk.
Table 5: Infrastructure and Interoperability for AI Scalability (PaloAlto Networks)
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Key Takeaways
- Enforce data standards (SNOMED, LOINC) to ensure interoperability.
- Focus on tangible AI use cases centered around the health consumer.
- Address governance challenges in AI decision-making and accountability.
- Invest in infrastructure for edge AI to decentralize and fast-track its deployment.
Defining the Problem: AI's Untapped Potential
“And AI is something at the heart of I'm I'm really keen to see, how we can touch the health consumers end of the day and not look at them as patients, but really as health consumers.”
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