Lecture: 4 hours/week
or
Hybrid: 2 hours/week in class and 2 hours/week online
In this course, students engage in a variety of learning activities such as lectures, case study analysis, independent research, exercises, training on data classification technology, participant presentations, classroom discussions and guest speakers.
- Exploration of the potential advantages and disadvantages of AI in healthcare
- Examination of the AI decision framework and application to current and future trends in healthcare
- Examination of ethical issues pertaining to AI in healthcare including inclusivity, equity and accountability
- Examination of governance issues related to AI in healthcare
- Exploration of equitable access and application of AI in healthcare
- Exploration of the role of big data in the development of AI systems and application of data ethics principles and practices
- Exploration of methods to ensure AI is responsive and sustainable
At the end of the course, the successful student will be able to:
- demonstrate an understanding of AI and machine learning applications and foundations;
- apply AI to monitor health outcomes and trends in healthcare;
- apply big data analytics in healthcare;
- analyze the benefits and challenges of AI and machine learning;
- apply patient risk stratification strategies to assess clinical workflows;
- demonstrate an understanding of the integrated approach to hospital management and systems optimization using AI.
The course evaluation is consistent with the ÌÇÐÄvlog´«Ã½Evaluation Policy. An evaluation schedule is presented at the beginning of the course. This is a graded course. All assignments must be completed to pass the course.
A list of required and optional textbooks, materials and electronic applications is provided for students at the beginning of each semester.