Speaker:  Xiaoqian Jiang – Professor, University of Texas Health Science Center at Houston

This presentation aims to provide a comprehensive overview of the latest advances in healthcare artificial intelligence (AI) and some dental informatics applications. We will delve into the foundational concepts of various machine learning models and their implications in the healthcare domain, with a focus on dentistry. The discussion will encompass convolutional neural networks (CNNs) and their role in image analysis, sequence models for processing sequential data, and recurrent neural networks (RNNs) for tackling time-series data. Furthermore, we will explore the attention mechanism, specifically the transformer architecture, and its impact on natural language processing tasks in healthcare. The presentation will also discuss discriminative and generative models, highlighting their respective strengths and weaknesses. In addition, we will discuss advanced generative models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs). Finally, we will examine the most recent technological breakthroughs and their implications for the future of AI-driven healthcare and dental informatics, emphasizing the importance of ongoing research and innovation in this rapidly evolving field.

Learning Objectives:

• Understand and differentiate various machine learning models in healthcare AI, with a focus on their applications in dental informatics: By the end of the presentation, participants should be able to describe and distinguish between CNNs, sequence models, RNNs, and attention mechanisms (specifically transformers), and recognize their relevance in healthcare and dental informatics.
• Comprehend the principles of discriminative and generative models, including advanced techniques like VAEs and GANs: Participants should be able to explain the core differences between discriminative and generative models, and describe how VAEs and GANs function, outlining their respective strengths and weaknesses in the context of healthcare AI applications.
• Evaluate the impact of recent technological breakthroughs and future prospects for AI-driven healthcare and dental informatics: Participants should be able to analyze and discuss the implications of emerging AI technologies in healthcare and dental informatics, recognizing the importance of continuous research and innovation in shaping the future of this rapidly evolving field.

Speaker Contact: xiaoqian.jiang@uth.tmc.edu