Artificial Intelligence in Healthcare (AIHC) – Certificate
- David Holmes III, Ph.D, Program Director
The Certificate track in Artificial Intelligence in Health Care is open only to Mayo Clinic employees who have a doctoral degree in a discipline applicable to research. Doctoral candidates may be considered. Potential candidates for the degree must hold Mayo Clinic appointments of sufficient duration to complete the program requirements.
Pre-Requisite Course Work
- Introduction to statistics: Data summarization and statistical testing (like CTSC 5600)
- Linear Algebra: Matrix Math
- Calculus: Single variable (“Calc 1”)
- Introduction to Scientific Programming (Python and/or R preferred)
Course Work
The curriculum for the Certificate consists of 12 credits. The student must complete all of the required courses listed below:
| Code | Title | Hours |
|---|---|---|
| Course Requirements | ||
| AIHC 5020 | Introduction to Data | 3 |
| AIHC 5030 | Introduction to Deployment, Adoption & Maintenance of Artificial Intelligence Models/Algorithms | 2 |
| CTSC 5300 | Foundations of Epidemiology | 1 |
| CTSC 5350 | Ethical Issues in Artificial Intelligence and Information Technologies | 1 |
| AIHC 6000 | Independent Study in Artificial Intelligence in Healthcare | 1 |
| Total Hours | 8 | |
| Code | Title | Hours |
|---|---|---|
| Electives | 4 | |
Select one of the following: | ||
| Introduction to Machine Learning | ||
| Applied Data Science and Artificial Intelligence in Pharmacology | ||
| Deep Learning for Medical Imaging | ||
Select one of the following: | ||
| Fundamentals of Statistics for Artificial Intelligence | ||
| Statistics: Linear Regression Concepts, Interpretation, and Statistical Software | ||
| Total Hours | 4 | |
Independent Study
The independent study is an opportunity to demonstrate the integration of knowledge from the concentration courses. Through the independent study with one of the faculty of the AIHC track, the learning will complete a project or writeup related to the use of AI in their scientific domain. The faculty and learner will meet at the beginning of the term to define the specific learning objectives and academic output from the Independent Study.
This is a suggested sequence based on a summer term start. Individual course plans may vary depending on true start date, program, employment/personal commitments, and research interests. Note that Certificate learners will choose between groups of courses to complete the certificate requirements. Be sure to confirm you have met your requirements using your degree planning tool. Course offerings may vary slightly. Current course offerings are posted in the course catalog.
| Code | Title | Hours |
|---|---|---|
| First Year - Summer Term | ||
| AIHC 5020 | Introduction to Data | 3 |
| Code | Title | Hours |
|---|---|---|
| First Year - Fall Term | ||
| AIHC 5615 | Fundamentals of Statistics for Artificial Intelligence | 2 |
| CTSC 5300 | Foundations of Epidemiology | 1 |
| CTSC 5610 | Statistics: Linear Regression Concepts, Interpretation, and Statistical Software | 3 |
| MPET 6450 | Applied Data Science and Artificial Intelligence in Pharmacology | 2 |
| Code | Title | Hours |
|---|---|---|
| First Year - Winter Term | ||
| AIHC 5010 | Introduction to Machine Learning | 3 |
| CTSC 5350 | Ethical Issues in Artificial Intelligence and Information Technologies | 1 |
| Code | Title | Hours |
|---|---|---|
| First Year - Spring Term | ||
| AIHC 6000 | Independent Study in Artificial Intelligence in Healthcare | 1-3 |
| AIHC 5030 | Introduction to Deployment, Adoption & Maintenance of Artificial Intelligence Models/Algorithms | 2 |
| BME 6720 | Deep Learning for Medical Imaging | 3 |