A bachelor’s degree in any field from an accredited college or university is acceptable for entrance into the graduate degree in artificial intelligence in medicine online program. However, the successful applicant will need an undergraduate grade point average of 3.0 or above (on a 4.0 scale). Students may be admitted provisionally with a 2.75 – 2.99 GPA but will need to maintain a 3.0 GPA or higher for all courses taken in the online MS in Artificial Intelligence in Medicine program to satisfy the degree requirements.
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Master the Future of AI
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Maddie Beck
Online Enrollment Counselor
Online Master of Science in Artificial Intelligence in Medicine
UofL’s 100% online Master of Science in Artificial Intelligence in Medicine (MS AIM) prepares medical professionals and engineers to effectively analyze medical problems and interpret complex data. As one of the few schools in the nation to offer this interdisciplinary degree, the goal is to equip future students for the next generation of healthcare technology through artificial intelligence methodologies, computational practices, and machine learning techniques.
In this 30 credit hour graduate degree program, students acquire expertise in artificial intelligence in medicine—including big data, medical imaging, biostatistics and experimental data. The highly specialized coursework is designed to provide graduates with the knowledge and skills needed to effectively analyze biomedical data, improve patient care and advance as leaders and innovators within healthcare.
This program is offered by the Department of Bioengineering in the J.B. Speed School of Engineering and the Department of Bioinformatics and Biostatistics in the School of Public Health & Information Sciences at the University of Louisville.
Online MS in Artificial Intelligence in Medicine Highlights
Academic Year Tuition
$830 per credit hour
$250 per credit hour active-duty tuition rate
This program is a Title IV federal financial aid eligible program. Tuition rate does not include costs associated with a specific course or program, such as textbooks.
Please note that other fees apply – check our tuition page for all applicable costs.
Tuition, fees, and charges are subject to change and effective on the date enacted.For additional information on educational expenses and the Cost of Attendance, please visit the Student Financial Aid Office website.
- Gain experience from courses taught 100% online by leaders in the bioengineering and biostatistics fields
- Skip the GRE— not required for admission into the master’s program
- Learn to implement artificial intelligence technologies to improve patient care and find innovative solutions to medical challenges
- Utilize data science and machine learning for research in healthcare and build a solid foundation in programming, software engineering, statistical analysis, and data storage and retrieval
- Recognize, formulate and apply sophisticated engineering, scientific and mathematical models to address the rapidly evolving medical challenges
START YOUR APPLICATION REQUEST INFORMATION
100% ONLINE COURSES
Complete your degree on your own time through fully online classes.
Learn More
30 CREDIT HOURS
Advance your degree and career with UofL’s innovative and 100% online MS in Artificial Intelligence in Medicine.
$250 Military Rate
Per-credit hour active-duty rate
"The Master of Science in Artificial Intelligence in Medicine prepares individuals to apply machine learning and artificial intelligence techniques to the analysis of big data, medical imaging, experimental data, and clinical and health care data."
Martin O'Toole
Program Coordinator, MS in Artificial Intelligence in Medicine
What can I do with a MS in Artificial Intelligence in Medicine?
The employment outlook for professionals with a degree in artificial intelligence in medicine is promising and continues to expand rapidly. With advancements in technology and an increasing demand for AI-driven solutions in healthcare, graduates can anticipate diverse career opportunities. From developing AI algorithms for medical diagnostics to implementing machine learning models in clinical settings, professionals in this field are poised to make significant contributions to improving patient care and advancing medical research. Additionally, the intersection of AI and medicine offers opportunities for interdisciplinary collaboration, paving the way for innovative breakthroughs in healthcare delivery.
Our successful graduates are well suited to apply their credentials toward the following roles and careers:
- Machine Learning Engineer
- Artificial Information Engineer
- Director of Medical Imaging
- Digital Solutions Engineer
- Personalized Medicine Directors
- Director of AI for Medical Engagement
- Account Executive for Artificial Intelligence in Medicine
- Senior DevOps Engineering
- Medical AI Research Scientist
- Healthcare Data Analyst
- Biomedical Informatics Engineer
- AI Healthcare Consultant
- Medical Imaging AI Engineer
- Health Informatics Specialist
- Machine Learning Healthcare Developer
- Clinical Decision Support Systems Developer
- Computational Biologist
| Preferred Application Deadline | Term | Start |
|---|---|---|
| August 1 | Fall Term I | Late August |
| November 15 | Spring Term I | January |
| April 1 | Summer Term I | May |
Note: We admit students on a rolling basis. The preferred deadlines help you complete the application process on time, be notified of acceptance, and enroll before the term begins. We review applications as they become complete and admit students for a specific term up to the day classes start. We recommend you work on and submit your complete application well in advance of the preferred deadline, as obtaining transcripts and other materials may take more time.
How to Apply for the Online Master of Science in Artificial Intelligence in Medicine
- Start your application for graduate admission
- Submit $65 non-refundable application fee
- Upload required materials*
- Request official transcripts from all previously attended colleges and universities. Transcripts are only accepted directly from the institution(s) by email: gradadm@louisville.edu (recommended) or mail: University of Louisville, Graduate School, 2211 S. Brook St., Louisville, KY 40292.
- Create a financial plan
START YOUR APPLICATION REQUEST INFORMATION
Online Master of Science in Artificial Intelligence in Medicine Admissions Requirements and Materials
To be considered for admission, applicants must have:
- a bachelor’s degree or its equivalent from an accredited institution
- a minimum undergraduate GPA of 3.0 on a 4.0 scale (applicants who do not meet the minimum GPA may inquire about conditional acceptance)
*Required application packet materials include:
- transcripts showing completion of a college-level statistics course and introduction to computer programming course
- a personal statement describing the applicant’s background and interest in artificial intelligence in medicine
- two letters of recommendation
*Your Application Portal:
Once you have started the graduate application, you can check the status and review any additional checklist to-do items. Log in to your application using the email address you used to apply for admission and your password. Your checklist items may include additional materials or documentation that facilitate a smooth admissions process. You will also have access to important contact information and next steps after an admissions decision is made.
Online International Applicants
The Master of Science in Artificial Intelligence in Medicine program is open to international students. If you live outside of the United States (U.S.) and intend to complete an online academic program from your home country, be sure to review these additional requirements for international students:
- All international transcripts must be certified as authentic and translated verbatim in English.
- Students whose primary language is not English must show English language proficiency by attaining one of the following:
- TOEFL scores of 80 or higher on the internet-based test OR
- Duolingo English test score of 105 or higher OR
- English proficiency can also be met by submitting official IELTS scores of at least 6.5 overall band score from the academic module exam
- Proof of degree earned from an accredited U.S. institution (requires provisional admission with evaluation of English language competency)
Note: students will not be issued a U.S. visa if admitted to the online program, since there are no campus attendance requirements.
Online Master of Science in Artificial Intelligence in Medicine Courses
The online Master of Science in Artificial Intelligence in Medicine is a 30 credit hour (10 course) program, with each course delivered in a 15-week course format (fall/spring) or 10-week (summer), 100% online. Each student must take 8 core courses (24 credit hours) and 2 elective courses (6 credit hours). Some of the courses are offered only in the spring, fall or summer semesters, therefore, it is important to work with your academic advisor on mapping out the best course for degree completion. Students will need to maintain at least a 3.0 GPA or higher for graduate courses to satisfy the degree requirements. All program requirements must be completed within six years of admission into the program.
| Core Course List (24 Credits) | Credit Hours |
|---|---|
| BE 540: Machine Learning in Medicine | 3 |
| BE 542: Medical Image Computing | 3 |
| BE 604: Introduction to AI in Medicine | 3 |
| BE 691: Non-thesis design/ Research project | 3 |
| PHMS 641: Data Mining | 3 |
| PHMS 642: Data Mining II | 3 |
| PHST 620: Introduction to Statistical Computing | 3 |
| PHST 680: Biostatistical Methods I | 3 |
| Total Core Credit Hours | 24 |
| Elective Courses (6 Credits) | |
| BE 530: Machine Learning in Python | 3 |
| BE 543: Computer Tools for Medical Image Analysis | 3 |
| BE 544: AI Techniques In Digital Pathology | 3 |
| BE 640: Computational Methods for Medical Image Analysis | 3 |
| BE 645: Artificial Intelligence and Radiomics | 3 |
| PHST 661: Probability | 3 |
| Total Elective Hours | 6 |
| Total Credit Hours Required | 30 |
START YOUR APPLICATION REQUEST INFORMATION
Core Course Descriptions
BE 540: Machine Learning in Medicine
Topics: 1) fundamentals of medical data, 2) application of machine learning models & algorithms to medicine, 3) learning from data & classification of disorders, and 4) overview of health data, collection with sensors, body area networks, brain image data and other publicly available medical applications data. Students will learn about machine learning applications to real world medical data through examples and reading papers. Students are expected to work on a team project and write technical reports.
BE 542: Medical Image Computing
Fundamentals of 2-D and 3-D image computing, application of image computing algorithms to medical images, enhancement and restoration of 2-D and 3-D medical data, and fundamentals of machine vision and medical data visualization. Students will learn image restoration, computer vision and visualization techniques with applications to medical data through examples and reading papers. Students are expected to work on a team project and write technical reports.
BE 604: Introduction to AI in Medicine
This course covers: 1) fundamentals of artificial intelligence, 2) solving problems by searching agents, 3) concepts of knowledge, logic, reasoning, and planning in AI, 4) machine learning concepts and different forms of learning and applications, and 5) data privacy and ethics in AI. Students will learn different ways to solve problems by automated searching approaches and using learning agents in machine learning with different applications in medicine. Students are expected to work on a team project and write technical reports.
BE 691: Non-thesis design/ Research project
Design or research project involving a literature search, project planning, design objectives, fabrication and/or experimentation, analysis, technical report writing, and oral presentation under a faculty member's guidance. Final technical report must be presented orally to course instructor and faculty mentor for graded evaluation.
Course Attribute(s): CBL - This course includes Community-Based Learning (CBL). Students will engage in a community experience or project with an external partner to enhance understanding and application of academic content.
PHMS 641: Data Mining I
The course is the first in a two-semester sequence graduate level introduction to data mining/big data analytics. It focuses on practical implementation and interpretation of the most common techniques in analysis of large datasets.
PHMS 642: Data Mining II (Prerequisite(s): PHMS 641)
This is the second of a two-semester graduate level course on data mining/big data analytics. It focuses on practical implementation and interpretation of the most common techniques in analysis of large datasets.
PHST 620: Introduction to Statistical Computing
This course provides an introduction to SAS. It will give students an overview of the SAS system under MS Windows and provide fundamental grounding in the environment for accessing, structuring, formatting and manipulating data. Students will learn how to summarize and display data, and the inference between data steps and procedures to get information out of data.
PHST 680: Biostatistical Methods I
A mathematically sophisticated presentation of statistical principles and methods. Topics include exploratory data analysis, graphical methods, point and interval estimation, hypothesis testing, and categorical data analysis. Matrix algebra is required. Data sets drawn from biomedical and public health literature will be analyzed using statistical computer packages.
Elective Course Decscriptions
BE 530: Machine Learning in Python
This course covers programming concepts in Python, machine learning concepts, and application of machine learning into biomedical and other problems using Python. Students will learn about the most applicable Python libraries that deal with different machine learning tools. Students are expected to work on a team project and write technical reports.
BE 543: Computer Tools for Medical Image Analysis
This course covers: 1) Essential computer software that can be used for handling all types of medical data, 2) advanced computer software that is used for medical image analysis, such as segmentation, registration, motion correction, etc., and 3) development of comprehensive computer-aided diagnosis systems based on these ready-to-go software packages.
BE 544: Artificial Intelligence Techniques in Digital Pathology
Prerequisite(s): BE 542 & skills in programming languages R and Python; or consent of instructor.This course provides both theoretical and practical information about computer vision and AI techniques required to process and analyze microscopic images as a part of the evolving transition to digital pathology. This evolution will enable the use of AI models in pathology to aid pathologists and healthcare professionals in the management and the diagnosis of different diseases.
BE 640: Computational Methods for Medical Image Analysis
This course covers the theory of stochastic and geometric models of medical imaging, including spatial interaction models, intensity models, and geometric shape models. The emphasis is on understanding the underlying mathematics in a practical sense.
BE 645: Artificial Intelligence and Radiomics
Artificial intelligence is comprehensively a bundle of cutting-edge computational algorithms that basically learn the patterns in the provided data to make prediction on new unseen data. Radiomics is almost a new terminology in the radiology area which means the extraction of large number of features from different kinds of medical images. This course couples both artificial intelligence and radiomics together to extract meaningful hidden quantitative data to be used in real word medical applications. This course also presents the basic concepts and applications of artificial intelligence in computer aided diagnostic systems.
PHST 661: Probability
Introduction to probability theory. Topics include axioms of probability, conditional probability, discrete and continuous random variables, probability distributions and joint distributions, moments, moment generating functions, mathematical expectation, transformations of random variables, limit theorems (Law of Large Numbers and Central Limit Theory).
-
Do I need a bachelor’s degree in a specific area?
-
Are the prerequisite courses offered online at UofL?
Yes, if a student has not completed the prerequisite courses in statistics and computer programming it is recommended that they work with Leigh Ann Elles, Assistant Director of Engineering Graduate Programs at leigh.elles@louisville.edu to discuss options for enrolling in the required prerequisite classes. Please note that some of the prerequisite courses at UofL online fill up quickly.
-
What skills will this program help me learn?
Earning your graduate degree in artificial intelligence in medicine will help enhance your ability to effectively analyze medical problems and interpret complex data. Through completion of this online program, you can acquire expertise in key areas of artificial intelligence—including computation, modeling and simulation, machine learning, big data and advanced statistical analysis—and their direct application to medical data management, medical imaging, disease progression modeling, experimental (clinical and laboratory) data and healthcare information.
-
Can international students apply for the online MS in Artificial Intelligence in Medicine at UofL?
International students who want to use a student visa are not currently eligible for this program. However, they can complete the degree if they are staying in their home country. Students will not be awarded an I-20 for this online program.
-
Can I complete the online MS in Artificial Intelligence in Medicine in one year?
While it is possible to complete the 30 credit hour program in 1 year, you would need to enroll full-time. That means 4 courses (or 12 credit hours) in the fall, 4 courses (or 12 credit hours) in the spring and 2 courses (or 6 credit hours) in the summer. We typically recommend that online students take 1-2 courses per semester (3 to 6 credit hours), especially when working simultaneously. Most students complete the program in 2 years.
Each student must take 8 core courses (24 credit hours) and 2 elective courses (6 credit hours). Some of the courses are offered only in the spring, fall or summer semesters, so you would need to work closely with your academic advisor on mapping out the best degree plan for completion for your needs.
Online MS in Artificial Intelligence in Medicine Highlights
Academic Year Tuition
$830 per credit hour
$250 per credit hour active-duty tuition rate
This program is a Title IV federal financial aid eligible program. Tuition rate does not include costs associated with a specific course or program, such as textbooks.
Please note that other fees apply – check our tuition page for all applicable costs.
Tuition, fees, and charges are subject to change and effective on the date enacted.For additional information on educational expenses and the Cost of Attendance, please visit the Student Financial Aid Office website.
- Gain experience from courses taught 100% online by leaders in the bioengineering and biostatistics fields
- Skip the GRE— not required for admission into the master’s program
- Learn to implement artificial intelligence technologies to improve patient care and find innovative solutions to medical challenges
- Utilize data science and machine learning for research in healthcare and build a solid foundation in programming, software engineering, statistical analysis, and data storage and retrieval
- Recognize, formulate and apply sophisticated engineering, scientific and mathematical models to address the rapidly evolving medical challenges
START YOUR APPLICATION REQUEST INFORMATION
100% ONLINE COURSES
Complete your degree on your own time through fully online classes.
Learn More
30 CREDIT HOURS
Advance your degree and career with UofL’s innovative and 100% online MS in Artificial Intelligence in Medicine.
$250 Military Rate
Per-credit hour active-duty rate
"The Master of Science in Artificial Intelligence in Medicine prepares individuals to apply machine learning and artificial intelligence techniques to the analysis of big data, medical imaging, experimental data, and clinical and health care data."
Martin O'Toole
Program Coordinator, MS in Artificial Intelligence in Medicine
What can I do with a MS in Artificial Intelligence in Medicine?
The employment outlook for professionals with a degree in artificial intelligence in medicine is promising and continues to expand rapidly. With advancements in technology and an increasing demand for AI-driven solutions in healthcare, graduates can anticipate diverse career opportunities. From developing AI algorithms for medical diagnostics to implementing machine learning models in clinical settings, professionals in this field are poised to make significant contributions to improving patient care and advancing medical research. Additionally, the intersection of AI and medicine offers opportunities for interdisciplinary collaboration, paving the way for innovative breakthroughs in healthcare delivery.
Our successful graduates are well suited to apply their credentials toward the following roles and careers:
- Machine Learning Engineer
- Artificial Information Engineer
- Director of Medical Imaging
- Digital Solutions Engineer
- Personalized Medicine Directors
- Director of AI for Medical Engagement
- Account Executive for Artificial Intelligence in Medicine
- Senior DevOps Engineering
- Medical AI Research Scientist
- Healthcare Data Analyst
- Biomedical Informatics Engineer
- AI Healthcare Consultant
- Medical Imaging AI Engineer
- Health Informatics Specialist
- Machine Learning Healthcare Developer
- Clinical Decision Support Systems Developer
- Computational Biologist
| Preferred Application Deadline | Term | Start |
|---|---|---|
| August 1 | Fall Term I | Late August |
| November 15 | Spring Term I | January |
| April 1 | Summer Term I | May |
Note: We admit students on a rolling basis. The preferred deadlines help you complete the application process on time, be notified of acceptance, and enroll before the term begins. We review applications as they become complete and admit students for a specific term up to the day classes start. We recommend you work on and submit your complete application well in advance of the preferred deadline, as obtaining transcripts and other materials may take more time.
How to Apply for the Online Master of Science in Artificial Intelligence in Medicine
- Start your application for graduate admission
- Submit $65 non-refundable application fee
- Upload required materials*
- Request official transcripts from all previously attended colleges and universities. Transcripts are only accepted directly from the institution(s) by email: gradadm@louisville.edu (recommended) or mail: University of Louisville, Graduate School, 2211 S. Brook St., Louisville, KY 40292.
- Create a financial plan
START YOUR APPLICATION REQUEST INFORMATION
Online Master of Science in Artificial Intelligence in Medicine Admissions Requirements and Materials
To be considered for admission, applicants must have:
- a bachelor’s degree or its equivalent from an accredited institution
- a minimum undergraduate GPA of 3.0 on a 4.0 scale (applicants who do not meet the minimum GPA may inquire about conditional acceptance)
*Required application packet materials include:
- transcripts showing completion of a college-level statistics course and introduction to computer programming course
- a personal statement describing the applicant’s background and interest in artificial intelligence in medicine
- two letters of recommendation
*Your Application Portal:
Once you have started the graduate application, you can check the status and review any additional checklist to-do items. Log in to your application using the email address you used to apply for admission and your password. Your checklist items may include additional materials or documentation that facilitate a smooth admissions process. You will also have access to important contact information and next steps after an admissions decision is made.
Online International Applicants
The Master of Science in Artificial Intelligence in Medicine program is open to international students. If you live outside of the United States (U.S.) and intend to complete an online academic program from your home country, be sure to review these additional requirements for international students:
- All international transcripts must be certified as authentic and translated verbatim in English.
- Students whose primary language is not English must show English language proficiency by attaining one of the following:
- TOEFL scores of 80 or higher on the internet-based test OR
- Duolingo English test score of 105 or higher OR
- English proficiency can also be met by submitting official IELTS scores of at least 6.5 overall band score from the academic module exam
- Proof of degree earned from an accredited U.S. institution (requires provisional admission with evaluation of English language competency)
Note: students will not be issued a U.S. visa if admitted to the online program, since there are no campus attendance requirements.
Online Master of Science in Artificial Intelligence in Medicine Courses
The online Master of Science in Artificial Intelligence in Medicine is a 30 credit hour (10 course) program, with each course delivered in a 15-week course format (fall/spring) or 10-week (summer), 100% online. Each student must take 8 core courses (24 credit hours) and 2 elective courses (6 credit hours). Some of the courses are offered only in the spring, fall or summer semesters, therefore, it is important to work with your academic advisor on mapping out the best course for degree completion. Students will need to maintain at least a 3.0 GPA or higher for graduate courses to satisfy the degree requirements. All program requirements must be completed within six years of admission into the program.
| Core Course List (24 Credits) | Credit Hours |
|---|---|
| BE 540: Machine Learning in Medicine | 3 |
| BE 542: Medical Image Computing | 3 |
| BE 604: Introduction to AI in Medicine | 3 |
| BE 691: Non-thesis design/ Research project | 3 |
| PHMS 641: Data Mining | 3 |
| PHMS 642: Data Mining II | 3 |
| PHST 620: Introduction to Statistical Computing | 3 |
| PHST 680: Biostatistical Methods I | 3 |
| Total Core Credit Hours | 24 |
| Elective Courses (6 Credits) | |
| BE 530: Machine Learning in Python | 3 |
| BE 543: Computer Tools for Medical Image Analysis | 3 |
| BE 544: AI Techniques In Digital Pathology | 3 |
| BE 640: Computational Methods for Medical Image Analysis | 3 |
| BE 645: Artificial Intelligence and Radiomics | 3 |
| PHST 661: Probability | 3 |
| Total Elective Hours | 6 |
| Total Credit Hours Required | 30 |
START YOUR APPLICATION REQUEST INFORMATION
Core Course Descriptions
BE 540: Machine Learning in Medicine
Topics: 1) fundamentals of medical data, 2) application of machine learning models & algorithms to medicine, 3) learning from data & classification of disorders, and 4) overview of health data, collection with sensors, body area networks, brain image data and other publicly available medical applications data. Students will learn about machine learning applications to real world medical data through examples and reading papers. Students are expected to work on a team project and write technical reports.
BE 542: Medical Image Computing
Fundamentals of 2-D and 3-D image computing, application of image computing algorithms to medical images, enhancement and restoration of 2-D and 3-D medical data, and fundamentals of machine vision and medical data visualization. Students will learn image restoration, computer vision and visualization techniques with applications to medical data through examples and reading papers. Students are expected to work on a team project and write technical reports.
BE 604: Introduction to AI in Medicine
This course covers: 1) fundamentals of artificial intelligence, 2) solving problems by searching agents, 3) concepts of knowledge, logic, reasoning, and planning in AI, 4) machine learning concepts and different forms of learning and applications, and 5) data privacy and ethics in AI. Students will learn different ways to solve problems by automated searching approaches and using learning agents in machine learning with different applications in medicine. Students are expected to work on a team project and write technical reports.
BE 691: Non-thesis design/ Research project
Design or research project involving a literature search, project planning, design objectives, fabrication and/or experimentation, analysis, technical report writing, and oral presentation under a faculty member's guidance. Final technical report must be presented orally to course instructor and faculty mentor for graded evaluation.
Course Attribute(s): CBL - This course includes Community-Based Learning (CBL). Students will engage in a community experience or project with an external partner to enhance understanding and application of academic content.
PHMS 641: Data Mining I
The course is the first in a two-semester sequence graduate level introduction to data mining/big data analytics. It focuses on practical implementation and interpretation of the most common techniques in analysis of large datasets.
PHMS 642: Data Mining II (Prerequisite(s): PHMS 641)
This is the second of a two-semester graduate level course on data mining/big data analytics. It focuses on practical implementation and interpretation of the most common techniques in analysis of large datasets.
PHST 620: Introduction to Statistical Computing
This course provides an introduction to SAS. It will give students an overview of the SAS system under MS Windows and provide fundamental grounding in the environment for accessing, structuring, formatting and manipulating data. Students will learn how to summarize and display data, and the inference between data steps and procedures to get information out of data.
PHST 680: Biostatistical Methods I
A mathematically sophisticated presentation of statistical principles and methods. Topics include exploratory data analysis, graphical methods, point and interval estimation, hypothesis testing, and categorical data analysis. Matrix algebra is required. Data sets drawn from biomedical and public health literature will be analyzed using statistical computer packages.
Elective Course Decscriptions
BE 530: Machine Learning in Python
This course covers programming concepts in Python, machine learning concepts, and application of machine learning into biomedical and other problems using Python. Students will learn about the most applicable Python libraries that deal with different machine learning tools. Students are expected to work on a team project and write technical reports.
BE 543: Computer Tools for Medical Image Analysis
This course covers: 1) Essential computer software that can be used for handling all types of medical data, 2) advanced computer software that is used for medical image analysis, such as segmentation, registration, motion correction, etc., and 3) development of comprehensive computer-aided diagnosis systems based on these ready-to-go software packages.
BE 544: Artificial Intelligence Techniques in Digital Pathology
Prerequisite(s): BE 542 & skills in programming languages R and Python; or consent of instructor.This course provides both theoretical and practical information about computer vision and AI techniques required to process and analyze microscopic images as a part of the evolving transition to digital pathology. This evolution will enable the use of AI models in pathology to aid pathologists and healthcare professionals in the management and the diagnosis of different diseases.
BE 640: Computational Methods for Medical Image Analysis
This course covers the theory of stochastic and geometric models of medical imaging, including spatial interaction models, intensity models, and geometric shape models. The emphasis is on understanding the underlying mathematics in a practical sense.
BE 645: Artificial Intelligence and Radiomics
Artificial intelligence is comprehensively a bundle of cutting-edge computational algorithms that basically learn the patterns in the provided data to make prediction on new unseen data. Radiomics is almost a new terminology in the radiology area which means the extraction of large number of features from different kinds of medical images. This course couples both artificial intelligence and radiomics together to extract meaningful hidden quantitative data to be used in real word medical applications. This course also presents the basic concepts and applications of artificial intelligence in computer aided diagnostic systems.
PHST 661: Probability
Introduction to probability theory. Topics include axioms of probability, conditional probability, discrete and continuous random variables, probability distributions and joint distributions, moments, moment generating functions, mathematical expectation, transformations of random variables, limit theorems (Law of Large Numbers and Central Limit Theory).
