CAPTIONS ARE AUTOGENERATED Discover how causal AI could revolutionize the healthcare industry forever. Learn about the impact of generative AI in data analysis and machine learning for healthcare and pharmaceuticals. summary In this conversation, Kaushik Trivedi, John Moran, and Ben Phillips delve into the transformative potential of Causal AI in the pharmaceutical industry. They discuss the differences between Causal AI and Generative AI, the challenges of AI adoption, and the importance of explainability in AI models. The discussion highlights real-world applications of Causal AI, particularly in understanding complex data environments and improving decision-making in healthcare. The speakers emphasize the need for a robust understanding of causality to navigate the intricacies of healthcare data and the future possibilities that Causal AI holds for enhancing patient outcomes and operational efficiency. Chapters 00:00 Understanding the Need for Causal AI in Healthcare 02:41 The Role of Causal AI vs. Generative AI 05:46 Exploring the Challenges of Generative AI 08:27 The Promise and Pitfalls of Agentic AI 10:58 Simplifying Causal AI for Non-Experts 14:07 Real-World Applications of Causal AI 16:35 Case Study: Causal AI in Market Dynamics 19:05 The Importance of Explainability in AI Models 27:36 Navigating the Challenges of NBA Execution 32:30 Understanding Causal AI and Its Explainability 37:03 The Unique Value of Causal AI in Pharma 42:00 Building a Center of Excellence for AI Adoption 43:23 Overcoming Barriers to AI Adoption 48:24 Future Possibilities with Causal AI