Online Artificial Intelligence in Digital Health Certificate

College of Medicine
Discover the benefits of AI in health care and how computational medicine is transforming this industry. 

Ohio State’s online Artificial Intelligence in Digital Health certificate provides a comprehensive, in-depth look at the role of AI in medicine. With a focus on practical applications and real-world examples, students will gain the knowledge and skills needed to excel as data scientists and project managers within the healthcare or IT industries.

This graduate online artificial intelligence certificate is designed for students with computer science or biomedical backgrounds aspiring to learn about artificial intelligence in medicine and healthcare professionals who want to learn AI technology. Course content focuses on the future of AI in health care. Learn how data is generated, collected, and annotated; use existing or develop new AI/ML models for health care data analytics; and effectively communicate and disseminate knowledge in any science or engineering domain. 

Campus Requirements: NONE – 100% Online 

Class Format: Asynchronous, complete your courses on your own schedule. 

Credit Hours Required: 12 

Cost Per Credit Hour: $812.07 (includes instructional and general fees). See the full cost breakdown here.

Admission Requirements: Bachelor’s degree and two courses in computer science, math, statistics, analytics, or similar; or bachelor’s degree and two years of professional experience in data science.  

Time to Completion: 2 semesters 

Application Deadline: July 31 to begin Autumn 2024. Apply now.

Sample Courses

BMI 5780

Programming for Biomedical Informatics

Students will learn the foundational and working knowledge, skills, and tools for programming Biomedical informatics.
BMI 5552

AI/ML Applications in Medical Imaging

Students gain an introductory knowledge of how AI can be used in medical image processing applications.
BMI 5553

Predictive Analytics in Electronic Health Records

As EHR databases are becoming more standardized and integrated across multiple hospital systems, they are gaining increasing attention from the informatics community as a resource to be mined, for example, to assess quality of patient care, develop early prediction models for disease, and define disease phenotypes.
BMI 5554

Natural Language Processing in Biomedical Informatics

This course introduces trainees to the natural language processing and text mining on biomedical data, including clinical notes from electronic medical records and biomedical literature.

Featured Faculty

Associate Professor
Associate Professor

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