Credits
This is a 3-credit course.
Description
Provides an in-depth exploration of artificial intelligence (AI) and its applications in clinical toxicology. Students learn the fundamentals of AI, machine learning and programming, deep learning and large language model evaluation and validation, focusing on real-world applications in clinical toxicology. Includes theoretical knowledge and practical skills, preparing students to integrate AI into clinical toxicology practice.
Prerequisites
Basic Python programming is preferred, and permission from the home institution is required to count this course as an elective toward their field of study.
Topics
| Module | Topic |
|---|---|
| Module 1 | Introduction to AI and Clinical Toxicology |
| Module 2 | Programming for AI |
| Module 3 | Data Handling and Preprocessing |
| Module 4 | Machine Learning Basics |
| Module 5 | Traditional Supervised Learning in Clinical Toxicology |
| Module 6 | Regression Techniques in Clinical Toxicology |
| Module 7 | Performance Metrics in Clinical Toxicology |
| Module 8 | Model Evaluation and Validation |
| Module 9 | Deep Learning for Tabular Data |
| Module 10 | Deep Learning for Tabular Data |
| Module 11 | Basic NLP |
| Module 12 | Applied Machine Learning in Clinical Toxicology |
| Module 13 | Large Language Models in Clinical Toxicology |
| Module 14 | ChatGPT and Conversational AI in Clinical Toxicology |
Materials
Required Materials
Artificial Intelligence In Medicine, Peter Szolovits (ed.), 2019, 1st edition, Routledge, ISBN: 9780429052071, available through UF HSC library.
Artificial Intelligence and Machine Learning in Health Care and Medical Sciences, Gyorgy J. Simon, Constantin Aliferis (eds.), 2024, 1st edition, Springer, ISBN: 978-3-031-39354-9, available through UF HSC library.
Library Access
Distance Education and UF Online Students enjoy the same library privileges as on-campus students.
To utilize the University of Florida Library System, click here!