Artificial Intelligence in Health Care (AIHC) – Postdoctoral Master's
- David R. Holmes, III Ph.D., Program Director
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)
Application
Candidates must complete a Postdoctoral Master’s Degree Application form. This form is available on the MCGSBS Master’s Programs intranet site. Supporting documents include a program fee agreement form, transcripts from previous colleges, and two letters of recommendation - one preferred from your direct supervisor/manager.
Eligibility
Applicants must be employed at Mayo Clinic. The employment appointment, as documented at the time of application, must be greater in length than the time required for completion of all requirements of the program. Eligible roles include: Mayo Clinic physician, scientist, fellow or resident with a doctoral degree in a discipline applicable to clinical research, or medical student who plans to have a research career (except for CTS). Visiting research fellows are eligible. However, visiting clinicians, research trainees, and research collaborators are not eligible.
Time Requirement
Applicants must have adequate protected time to complete the course and research requirements within the designated program length. Applications with inadequate protected time to complete the program will not be accepted. Time to completion can vary by program and Mayo Clinic role from two to five years. All scholars must be in their program for a minimum of one year in to meet the MCGSBS residency requirement. Scholars must complete all program requirements within five years.
Students must have dedicated time for their program commitments and abide by course attendance requirements as defined in course syllabi. Students must be appropriately engaged in their program and demonstrate continued progress toward graduation.
Registration Requirement
At least 75% of the coursework for the Master’s degree must be completed in MCGSBS. It is expected that a minimum of one year will be devoted to research.
Minimum Credit Requirements
Students must complete a minimum of 45 credits, which includes a maximum of 16 Research credits. (See Requirements tab within each track for specific course requirements.)
Transfer Credits
A total of 9 didactic credits may be transferred into the program. For more details, see the Credit Conversion, Transfer, Waiver, and Substitution Policy on the MCGSBS intranet site.
Course Requirements
The curriculum for the Master’s degree consists of 45 credits. The student must complete all of the required courses listed below:
| Code | Title | Hours |
|---|---|---|
| Research 1 | ||
| MGS 6100 | Master's Thesis Proposal | 3 |
| MGS 6840 | Master's Research (4 cr/qtr - 4 qtrs required) | 16 |
| Track Requirements | ||
| AIHC 5010 | Introduction to Machine Learning | 3 |
| AIHC 5020 | Introduction to Data | 3 |
| AIHC 5030 | Introduction to Deployment, Adoption & Maintenance of Artificial Intelligence Models/Algorithms | 2 |
| AIHC 5200 | AI Math Foundations | 2 |
| AIHC 5500 | Artificial Intelligence and Machine Learning Journal Club | 1 |
| CTSC 5300 | Foundations of Epidemiology | 1 |
| CTSC 5350 | Ethical Issues in Artificial Intelligence and Information Technologies | 1 |
| AIHC 5615 | Fundamentals of Statistics for Artificial Intelligence | 2 |
| Concentration | ||
| Select 6 credits from the listed concentrations | 6 | |
| Advanced Coursework | ||
| Select 5 credits 2 | 5 | |
| Total Hours | 45 | |
- 1
It is expected that a minimum of one year will be devoted to research.
- 2
Students can choose to focus their elective credits into one of the concentration areas listed below or select their elective credits based on recommendations from their advisor or based on their professional goals.
Concentrations Requirements
Signals & Systems (SS)
| Code | Title | Hours |
|---|---|---|
| BME 6720 | Deep Learning for Medical Imaging | 3 |
| BME 5704 | Bioinstrumentation and Signal Processing | 3 |
| Total Hours | 6 | |
Discovery Science (DS)
| Code | Title | Hours |
|---|---|---|
| Choose 6 credits from the options provided. | 6 | |
| Molecular Variant Evaluation | ||
| Genomic Analysis of Complex Traits | ||
| Genomic Analysis and Data Interpretation for Rare and Undiagnosed Diseases | ||
| Laboratory Methods in Biomedical Image Processing | ||
| Introduction to Bioinformatics Concepts and Core Technologies for Individualized Medicine Approaches | ||
| Applied Data Science and Artificial Intelligence in Pharmacology | ||
| Total Hours | 6 | |
Discovery Science - Molecular Genetics (DSMG)
| Code | Title | Hours |
|---|---|---|
| BMB 5450 | Genomics and Functional Genomics | 3 |
| CTSC 5400 | Introduction to Bioinformatics Concepts and Core Technologies for Individualized Medicine Approaches | 1 |
| MPET 6450 | Applied Data Science and Artificial Intelligence in Pharmacology | 2 |
| Total Hours | 6 | |
Applied Clinical Informatics (CI)
| Code | Title | Hours |
|---|---|---|
| AIHC 5960 | Introduction to Medical Informatics | 2 |
| AIHC 5961 | Health Information Technology Evaluation: Clinical Informatics Methods | 1 |
| AIHC 5962 | Clinical Surveillance, Alerting, and Data Representation | 1 |
| AIHC 5963 | Health Information Security | 1 |
| AIHC 5964 | AI & HIT Implementation | 1 |
| Total Hours | 6 | |
Translational & Regulatory (TR)
| Code | Title | Hours |
|---|---|---|
| AIHC 5045 | Healthcare Software & AI Management: Quality, Safety, & Implementation | 1 |
| CTSC 5020 | Regulatory Issues in Clinical Research | 1 |
| CTSC 5025 | Introduction to Regulatory Science | 1 |
| CTSC 6120 | Case Studies in Translation | 2 |
| CTSC 5400 | Introduction to Bioinformatics Concepts and Core Technologies for Individualized Medicine Approaches | 1 |
| CTSC 5720 | Clinical Trials Design and Conduct | 1 |
| Total Hours | 7 | |
Written Qualifying Exam (WQE)
The WQE is designed to demonstrate a student’s ability to integrate and synthesize the core competencies of the program. Students must pass the WQE to complete the degree requirements.
Thesis Defense (Final Oral Examination)
The final oral examination cannot be completed until the following criteria have been met:
- The Written Qualifying Examination has been passed,
- All coursework has been completed with a GPA of 3.0 or higher,
- All program milestones have been met, and
- AIHC Postdoctoral Programs Committee has reviewed and approved the thesis proposal
Publication Requirement
Master’s thesis research must make a substantial contribution to the biomedical literature, and preparing work for publication is an important part of research training. The expectation is that thesis research will result in multiple publications. To graduate, students need to publish at least two original peer-reviewed papers on which they are first author.
Suggested Sequence
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 this sequence only covers the Core courses and Concentration courses. Students are responsible for additional elective credits which are required by the program. 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 |
| CTSC 5300 | Foundations of Epidemiology | 1 |
| Concentration | 1 | |
| Regulatory Issues in Clinical Research | ||
| Introduction to Regulatory Science | ||
| Code | Title | Hours |
|---|---|---|
| First Year - Fall Term | ||
| AIHC 5200 | AI Math Foundations | 2 |
| AIHC 5615 | Fundamentals of Statistics for Artificial Intelligence | 2 |
| Concentration | 2 | |
| Introduction to Medical Informatics | ||
| Applied Data Science and Artificial Intelligence in Pharmacology | ||
| Code | Title | Hours |
|---|---|---|
| First Year - Winter Term | ||
| MGS 6100 | Master's Thesis Proposal | 3 |
| AIHC 5010 | Introduction to Machine Learning | 3 |
| CTSC 5350 | Ethical Issues in Artificial Intelligence and Information Technologies | 1 |
| Concentration | ||
| Genomic Analysis of Complex Traits | ||
| Molecular Variant Evaluation | ||
| Introduction to Bioinformatics Concepts and Core Technologies for Individualized Medicine Approaches | ||
| Clinical Trials Design and Conduct | ||
| Case Studies in Translation | ||
| Health Information Technology Evaluation: Clinical Informatics Methods | ||
| Clinical Surveillance, Alerting, and Data Representation | ||
| Code | Title | Hours |
|---|---|---|
| First Year - Spring Term | ||
| AIHC 5030 | Introduction to Deployment, Adoption & Maintenance of Artificial Intelligence Models/Algorithms | 2 |
| AIHC 5500 | Artificial Intelligence and Machine Learning Journal Club | 1 |
| Concentration | ||
| Health Information Security | ||
| AI & HIT Implementation | ||
| Healthcare Software & AI Management: Quality, Safety, & Implementation | ||
| Bioinstrumentation and Signal Processing | ||
| Deep Learning for Medical Imaging | ||
| Genomic Analysis and Data Interpretation for Rare and Undiagnosed Diseases | ||
| Code | Title | Hours |
|---|---|---|
| Second Year - All Terms | ||
| MGS 6840 | Master's Research (Enroll once per term for minimum 4 terms.) | 4 |