BME 499.060: AI in BME


The goal of this course is to introduce and apply AI tools to problems in BME.

AI and machine learning algorithms have had a major impact on biomedical science in the past decade. AI algorithms can learn patterns from biomedical data sets to provide actionable insights on disease diagnosis or treatment. This course will focus on practical applications of AI in BME with hands-on tutorials.

This course will provide an overview of a wide range of AI and machine-learning tools (clustering, regression, decision trees, random forests and neural networks), biomedical data sets (imaging, omics and data-mining) and diseases (cancer, infectious-, cardiovascular- and neurological-).

Software used - MATLAB. No programming experience necessary beyond Eng 101

Who is this for?

This course is intended for BME undergraduates and graduate students interested in learning AI

EECS and other students interested in biomedical applications of AI can also register if they satisfy the Prerequisites

This course is for 2 credit hours and will be on M/W from 3:30-4:30 in Dow 1010

Students need to bring their own laptops with MATLAB installed. MATLAB is available for free from CAEN.


Biology 172 or 174, Intro to Biology

Math 116, Calculus

Engineering 101, Intro to Computing

Statistics & Linear algebra (preferable, but not required)

Course outline

  1. Introduction to AI & Machine-learning (ML)

  2. Matlab programming boot camp

  3. Exploratory data analysis

  4. Plotting and data visualization

  5. Handling big-data sets

  6. Hypothesis testing

  7. Unsupervised learning

  8. Classifying cancer sub-types using clustering

  9. Regression

  10. Predicting cardiovascular risk using regression

  11. Model validation

  12. Decision trees

  13. Predicting diabetic retinopathy using decision trees

  14. Random Forests

  15. Identifying synergistic drug combinations using RF

  16. Neural Networks (NN)

  17. Diagnosing breast cancer from biopsy images using NN

  18. Interpreting AI

  19. Guest lecture - AI and Omics, Kelly Arnold

  20. Guest lecture - AI in Imaging, Jeff Fessler

  21. Guest lecture - AI in Neural Engineering, Cindy Chestek

  22. Presentation by student teams (2 lectures)


  1. Assignments (50%). There will be five assignments. The assignments will involve both programming modules and biomedical modules involving discussion of salient biomedical AI studies.

  2. Design project (50%). The final project will be done as a team of students. Each team is required to give a short presentation on their project along with a written report. Both the presentation and the report should provide a background of the biomedical problem, a description of the AI methods used, effective visualization of the results, and a discussion on possible limitations and future directions of the project.