Digital Transformation Track: The Fear of AI: Is It Real or Is It Fear of Missing Out?
Date & Time
Monday, April 15, 2024, 1:00 PM - 2:00 PM
Location Name
Osceola Ballroom C
Digital Transformation Track

Unravel the intricate process of bridging the AI chasm, a crucial step that connects the fear of the unknown with healthcare AI with real-world implementation. Understand how AI innovations can be translated into clinical settings, gain insights into practical applications and deployment in healthcare, and bridge the gap between innovation and implementation.

In this session, we will shed light on innovative applications of Generative AI in medical research, diagnostics, treatment planning, and healthcare management and explore the challenges, breakthroughs, and ethical considerations associated with employing Generative AI in healthcare.

Learning Objectives
1. Evaluate the Role of AI in Healthcare: Participants will gain a comprehensive understanding of the role and potential of AI in healthcare, including its applications in diagnosis, treatment, and patient care. They will explore how AI can improve operational efficiency, patient safety, and quality of care.

2. Identify AI Innovations in Patient Care: Participants will delve into the ways AI can enhance patient care, such as through personalized treatments, predictive modeling, and improving patient experiences. They will also discuss the ethical considerations and potential risks associated with AI in healthcare.

3. Analyze Operational Efficiency as a Result of AI: Participants will learn how AI can be leveraged to improve operational efficiency in healthcare settings. This includes automating repetitive tasks, reducing waste, and optimizing resource utilization. They will also examine case studies where AI has successfully improved operational efficiency.

4. Outline AI in Healthcare Settings: Participants will understand the practical challenges and strategies for implementing AI in healthcare. They will learn about the importance of interdisciplinary collaboration, the process of forming and managing data science teams, and the steps to ensure successful AI implementation.

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