Foundations of Artificial Intelligence in Health

Catalog description

Recent advances in artificial intelligence (AI) have revolutionized the practice of scientific and biomedical research. AI is often used interchangeably with the term 'machine learning', which itself is only one of the subfields within AI dealing with the broader concept of inductive reasoning. However, a wealth of key prerequisite topics that focus on deductive reasoning are central to the practice of AI in biomedical informatics. These founding principles and their intersection with biomedical informatics are the focus of this first course on artificial intelligence. This course is divided into modules that cover (1) introductory/background materials, (2) knowledge representation, (3) logic, (4) essentials of rule-based systems, (5) search, (6) information structure and inference, and (7) special topics. These topics offer a global foundation for the branches of AI in biomedicine and support a deeper understanding of inductive reasoning and machine learning. More broadly, we will explore how biomedical data can be organized, represented, interpreted, searched, and applied to derive knowledge, make decisions, and ultimately make predictions while avoiding bias. It is expected that students will be familiar with basic biomedical concepts, terminology, and statistics. Additionally, students should be competent in one or more computer programming languages (Python is preferred), and should be familiar with basic programming concepts including data structures, control flow, and I/O. It is recommended, but not required, that students have taken Introduction to Biomedical Informatics ( BMIN 5010 ) and Data Science for Biomedical Informatics ( BMIN 5030 ). No previous exposure to artificial intelligence is assumed.