Overview
This course is intended for students with an introductory background in the applications of artificial intelligence (AI) and machine learning (ML) techniques in pharmacy fields. The goal of the course is to provide the AI/ML concepts and methodology commonly used in pharmacy research and clinical practice covering all phases from drug discovery to the post-approval assessment of drug safety, effectiveness and value in real-world settings. Students will be exposed to several transcending concepts throughout the course that include real-world data sources, study design and measurement, basic analytical approaches, and practical research applications using AI/ML approaches in pharmacy.
- Credit(s): 3
- Course Format:
- Required: This is an elective course.
- (GMS 6224) Foundations in Precision Medicine: Medical Molecular Genetics
- (PHA 6134) Foundations in Precision Medicine: Genomic Technologies
- (PHA 6935) Foundations in Precision Medicine: Genetic Epidemiology
- (PHA 6935) Principles of Pharmacogenomics
Course Objectives
Upon completion of this course, the student will be able to:
- Describe the key applications of AI/ML techniques in pharmacy research and practice
- Recognize and differentiate supervised vs. unsupervised ML
- Articulate founding principles and concepts of commonly used ML approaches
- Evaluate and compare prediction performance across different ML models
- Distinguish AI/ML-related prediction and description from causal inference
- Recognize bias/fairness and ethical issues of AI/ML applications in pharmacy