The Critical Need for Explainable AI in Clinical Decision-Making

In an era where AI is transforming healthcare, the need for explainable AI has never been more critical. This presentation will explore how explainable AI enhances clinical decision-making by providing transparency and clarity in the recommendations generated. We will discuss the limitations of traditional black box models and demonstrate how white box AI technologies empower healthcare staff by enabling them to understand the underlying data and rationale behind AI-driven insights. Join us to discover how fostering trust through explainable AI can lead to improved patient outcomes and more effective clinical support.

Learning Objectives
1. Understanding Explainable AI -- Participants will gain a clear understanding of what explainable AI is and how it differs from traditional black box AI models, including the importance of transparency in clinical applications.
2. Benefits of Explainability -- Attendees will learn about the specific benefits of implementing explainable AI in healthcare, including enhanced decision-making, increased trust among clinicians, and improved patient outcomes.
3. Practical Applications -- Participants will explore real-world examples of how explainable AI is being used in clinical settings, showcasing successful implementations and the impact on staff workflows and patient care.

THIS SESSION IS NOT ELIGIBLE FOR CME CREDIT