Discover how digital twins can revolutionize health AI product development life cycle! Let's delve into how digital twins can streamline validation, slash development and validation expenses, and ensure product viability, resulting in accelerated launches. 

Digital twins are digital simulators of physical objects, systems, or processes, leveraging real-time data to model their behavior, performance, and unique attributes. Although the potential of AI to drive innovation in healthcare is promising, the current product development life cycle for such innovations involves multiple intertwined validation and testing stages to ensure their safety, effectiveness and applicability across varied populations or users. Consequently, this elongates the development life cycle compared to that of typical digital products, making it both time-consuming and more expensive. 

 Join us to: 

 Understanding Health AI Development Process 

1. Regular Lifecycle 
2. Challenges in Traditional Development Approaches 
3. How Digital Twins Can Address These Challenges 
4. Practical Exercise #1: Use Cases & Related Challenges 

 Discover Case Studies & Examples 

1. Designing, Testing & Implementing a Digital Twin 
2. Success Stories & Lessons Learnt 
3. Practical Exercise #2: Implementing Digital Twins in An Interactive Session 
4. Summary & Discussion 

 Discuss What is Next? Future Trends