Interdisciplinary Studies: Specialization in Artificial Intelligence in Medicine (PhD)
Admission Requirements
Applicants for interdisciplinary doctoral programs must present complete admission credentials and have an approved program of study in order to be formally admitted by the Graduate School.
- Complete graduate application.
- A 3.00 grade point average is recommended for admission, but applicants may be considered with a 2.75 grade point average under special conditions for admission.
- Completion of prerequisite coursework in calculus I, II, and III with coverage through multivariable calculus, and completion of an introductory course in computer programming (e.g., BE 340 or PHST 301 or equivalent).
- Proof of a Baccalaureate Degree and official transcripts of all undergraduate and graduate course work.
- International students for whom English is not their primary language must show English language proficiency by one of the following:
- TOEFL examination score 213 (computer-based test) or 79 (internet-based test)
- IELTS test score of 6.5 or higher
- Duolingo score of 105.
- PTE Academic score of 55 or higher.
- Demonstration of a degree awarded from a US-accredited English language institution.
- Submission of a written statement by the applicant describing previous experience related to Artificial Intelligence in Medicine and a statement as to how the PhD program will allow the student to fulfill their career goals.
- Two letters of recommendation from individuals who are able to comment on the student’s academic abilities and a potential for success in graduate studies.
Program of Study
Course requirements in the Interdisciplinary PhD Program in Artificial Intelligence in Medicine and Health consist of 36 credits from core courses and 9 credits are from elective courses. The suggested full-time course of study is below.
| Year 1 | ||
|---|---|---|
| Fall | Hours | |
| BE 604 | Introduction to Artificial Intelligence in Bioengineering 1 | 3 |
| PHST 620 | Introduction to Statistical Computing 1 | 3 |
| PHMS 641 | Data Mining I 1 | 3 |
| Hours | 9 | |
| Spring | ||
| BE 603 | Bioengineering Research Ethics | 2 |
| BE 540 | Machine Learning in Medicine 1 | 3 |
| PHMS 642 | Data Mining II 1 | 3 |
| Hours | 8 | |
| Summer | ||
| BE 555 | Large Language Models for Healthcare and Medicine | 3 |
| Elective (see list below) | 3 | |
| Hours | 6 | |
| Year 2 | ||
| Fall | ||
| BE 601 | Bioengineering Seminar | 1 |
| PHST 661 | Probability | 3 |
| BE 544 | Artificial Intelligence Techniques in Digital Pathology | 3 |
| Elective (see list below) | 3 | |
| Hours | 10 | |
| Spring | ||
| BE 601 | Bioengineering Seminar | 1 |
| PHST 662 | Mathematical Statistics | 3 |
| PHST 681 | Biostatistical Methods II | 3 |
| BE 692 | Bioengineering Clinical Rotation | 2 |
| Elective (see list below) | 3 | |
| Hours | 12 | |
| Minimum Total Hours | 45 | |
- 1
Course required for obtaining a Master's degree during the PhD program.
Potential Elective Courses
| Code | Title | Hours |
|---|---|---|
| BE 524 | LabVIEW for Bioengineers | 3 |
| BE 530 | Machine Learning in Python | 3 |
| BE 542 | Medical Image Computing | 3 |
| BE 543 | Computer Tools for Medical Image Analysis | 3 |
| BE 581 | Advanced Computer-Aided Design and Manufacturing for Bioengineers | 3 |
| BE 640 | Computational Methods for Medical Image Analysis | 3 |
| BE 645 | Artificial Intelligence and Radiomics | 3 |
| BE 685 | Modeling of Biological Phenomena | 3 |
| CSE 532 | Python and Data Analytics | 3 |
| CSE 536 | Data Management and Analysis | 3 |
| CSE 538 | Graph Database and Graph Analytics | 3 |
| CSE 545 | Artificial Intelligence | 3 |
| CSE 546 | Introduction to Machine Learning | 3 |
| CSE 547 | Deep Learning Algorithms and Methods | 3 |
| CSE 590 | Special Topics in Computer Science and Engineering | 1-6 |
| CSE 609 | Multimedia Processing | 3 |
| CSE 619 | Design and Analysis of Computer Algorithms | 3 |
| CSE 620 | Combinatorial Optimization and Modern Heuristics | 3 |
| CSE 622 | Simulation and Modeling of Discrete Systems | 3 |
| CSE 628 | Computer Graphics | 3 |
| CSE 641 | Medical Imaging Systems | 3 |
| CSE 645 | Advanced Artificial Intelligence | 3 |
| CSE 660 | Introduction to Bioinformatics | 3 |
| ECE 520 | Digital Signal Processing | 3 |
| ECE 521 | Digital Signal Processing Laboratory | 1 |
| ECE 528 | Deep Learning and AI Tools | 3 |
| ECE 529 | Deep Learning and AI Tools Laboratory | 1 |
| ECE 543 | Fundamentals of Microfabrication | 3 |
| ECE 544 | Microfabrication Laboratory | 1 |
| ECE 564 | Fundamentals of Autonomous Robots | 3 |
| ECE 565 | Fundamentals of Autonomous Robots Lab | 1 |
| ECE 613 | Computational Intelligence Methods for Data Analysis | 3 |
| ECE 614 | Deep Learning | 3 |
| ECE 618 | Artificial Intelligence Systems | 3 |
| ECE 619 | Computer Vision | 3 |
| ECE 636 | MEMS Design and Fabrication | 4 |
| ECE 643 | Introduction to Biomedical Computing | 3 |
| ECE 645 | Computer Vision Laboratory | 1 |
| ISE 664 | Experimental Design in Engineering | 3 |
| PHST 650 | Advanced Topics in Biostatistics | 1-3 |
| PHST 655 | Basic Statistical Methods for Bioinformatics | 3 |
| PHST 680 | Biostatistical Methods I | 3 |
| PHST 682 | Multivariate Statistical Analysis | 3 |
| PHST 684 | Categorical Data Analysis | 3 |
| PHST 710 | Advanced Statistical Computing I | 3 |
| PHST 711 | Advanced Statistical Computing II | 3 |
| PHST 750 | Statistics for Bioinformatics | 3 |
| PHST 752 | Statistical Genetics | 3 |
| PHST 762 | Advanced Statistical Inference | 3 |
| PHST 782 | Generalized Linear Models | 3 |
| PHST 785 | Nonlinear Regression | 3 |
| PHST 791 | Bayesian Inference and Decision | 3 |
| PHMS 644 | Biomedical Foundations for Health Analytics | 3 |
| PHMS 670 | Statistical Data Management | 3 |
| PHMS 671 | Statistical Analysis for Population Health | 3 |
| PHMS 694 | Innovation and Entrepreneurship in Healthcare | 3 |
