Generative AI in Healthcare and why data management matters | Webinar now on demand

Introduction to how generative AI can transform healthcare by streamlining tasks, enhancing patient care, and the importance of data management and architecture.

Explanation of application widgets for viewers, resizing options, Q&A widget usage during the presentation, and email response for unanswered questions.

Introduction of three leading experts in healthcare technology, their roles, experiences, and contributions to the industry.

Discussion on the state of generative AI in the healthcare landscape, focusing on practical transformation, data science, and the convergence of key developments.

Explanation of use cases of generative AI in healthcare, including agent assist, customer experience improvement, care management, personalized patient outreach, unstructured data analysis, and a demo of a concept discovery engine.

Overview of the ecosystem supporting generative AI, including infrastructure, model providers, data transformation providers, and AI-powered transformation providers.

Discussion of challenges in data management for generative AI, emphasizing data quality, integration, governance, and scalability, and the importance of a strong foundation for generative AI programs.

Conclusion highlighting the importance of understanding data, industry expertise, and implementing solutions at scale for success in generative AI, along with a focus on security and innovation.

Exploring the importance of prompt engineering and mastering prompt repositories to chain frameworks effectively, benefiting various companies leveraging retrieval augmented Generation tools for data focus and context.

Discussing the significance of fine-tuning for enhanced applicability and the benefits of developing open-source large language models (LLM) in terms of open versus closed solutions.

Addressing the criteria for prioritizing generative AI use cases based on alignment with business strategy, data considerations, risk assessment, talent and technology resources, and collaboration between business and tech leaders.

Factors such as data type, access level, security, external data requirements, risk assessment, talent availability, and technology alignment play a crucial role in prioritizing generative AI use cases.

Exploring the investment required for implementing generative AI applications, emphasizing the importance of measuring operational efficiency and success metrics to determine the long-term cost-effectiveness of such solutions.