Demystifying AI in Healthcare: A Practical Survey of Machine Learning Technologies 0599
Saturday, November 2, 2024
10:30 AM – 11:30 AM
Location: ROOM: Obelisk A REGION: Atrium II Mezzanine Level >>> DIRECTIONS: Exit Atrium Elevators. Take a right and proceed down the corridor. Continue past the escalators and the row of rooms on your left. At the end of the row of rooms, turn down the corridor on the right. Obelisk A is the first room on the left. If coming from the Tower Mezzanine, from the Tower Elevators, turn right. Proceed down the stairs and take the first corridor on your left. Obelisk A is the first room on the left.
As big data has become more meaningful and valuable to practitioners and researchers, machine learning (ML) and artificial intelligence (AI) have ushered in a transformative era in the healthcare sector.
One of the greatest challenge to AI is not limitations in the technologies available, but ensuring their adoption into clinical practice. To complicate matters, AI is not one technology, but more often a collection of methods and tools.
In this symposium presentation, discussion will include popular machine learning techniques driving AI in healthcare. Additionally, AI technologies will be broken down to key elements and core technological processes to illustrate their advantages and realistic boundaries.
A second goal of this lecture is to empower the audience to pursue more in-depth knowledge of healthcare AI technologies to give strategies to identify areas of strength and weakness of these technologies within their organizations.
Learning Objectives:
Upon completion, participants will be able to define Machine Learning
Upon completion, participants will be able to define AI
Upon completion, participants will be able to describe Role of AI in healthcare
Upon completion, participants will be able to describe how AI is organized and function
Upon completion, participants will be able to describe the advantages and limitations of AI in the healthcare space
Upon completion, participants will be able to define Machine Learning
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