Summary
The panel discusses the current state and future of AI in clinical decision-making, focusing on imaging, personalization, and workforce evolution. They highlight the need for better data, validation, and governance to ensure responsible AI implementation and improve patient outcomes. Performance of AI algorithms needs improvement before wide-scale adoption.
Key Topics
4 key topics from this session at Future Health Summit (FHS). Thicker branches were mentioned more often in the talk.
Table 9. AI in Clinical Decision-Making and Workforce Evolution (PwC)
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Key Takeaways
- Focus on AI applications that demonstrably improve speed and accuracy.
- Prioritize clinical validation and diverse datasets for AI algorithms.
- Develop AI solutions that personalize treatment and improve adherence.
- Address AI performance issues before broad commercialization to maintain trust.
Reality Check: AI Use Cases & Challenges
“The real issue is the performance of the current technology in the environment we are seeing. Now I'm talking about the very specific use case of CNNs in radiology.”
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