AI in Medicine: Foundations and Applications in Medical Practice and Research

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AI in Medicine: Foundations and Applications in Medical Practice and Research Course Illustration.

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Explore the foundational principles of artificial intelligence (AI) and its transformative applications in medicine and biomedical research.

  • Online; Self-Paced

Enroll and gain access to course materials to complete at your own pace.

$495

Save and learn more with discounted multi‑course bundles or our all‑access HMX and HealthXcelerate Learning Pass.

Certificate

Earn a short course certificate of completion when you finish all coursework within 8 weeks.

3-4 Hours

Most people can expect to complete short courses in 3-4 hours.

On This Page

Overview

AI is reshaping health care, offering unprecedented opportunities to improve patient outcomes, enhance clinical efficiency, and drive innovation in research. With the rapid adoption of AI in health care and research, it’s now more important than ever to understand the foundations and applications of this exciting field. 

This HMX short course enables you to learn directly from leading Harvard Medical School faculty about the technical foundations of AI, including supervised and self-supervised learning and generative modeling. You will gain insights into various AI pipelines, and how they can be applied to improve patient diagnosis, clinical workflows, and different types of research, as well as the current regulatory landscape of both traditional machine learning models and generative AI.  

Learning Objectives

  • Understand the key technical concepts of modern machine learning, including supervised learning, self-supervised learning, and generative modeling
  • Explore how AI is applied to assist with diagnosis, clinical workflows, and biomedical research
  • Learn about the pre-market evaluation and post-market surveillance strategies used to evaluate AI models
  • Understand the different regulatory frameworks for AI in medicine

Who Should Participate

Clinicians and health care professionals; researchers in biomedicine and technology sectors; and professionals a across health care fields

About the Course

HMX short courses feature targeted lessons on the latest medical science topics and advancements. Stay up-to-date on essential knowledge by exploring specialized topics in a shorter format designed for busy professionals.

Who Should Enroll

This course will benefit learners such as clinicians and other health care professionals who are interested in learning about current AI advances; biomedical researchers who are seeking a comprehensive understanding of AI technologies; professionals in managerial roles in health care and related fields who are keen to learn how AI could be incorporated into routine practices to improve delivery of care.

Course Format

Most people can expect to spend 3-4 hours total on an HMX short course. In order to be considered for a short course certificate of completion, you must complete your coursework within eight weeks.

Additional Course Information

For information around payments and policies, please review our frequently asked questions.

  • Key Technical Concepts in Machine Learning: Supervised Learning
  • Generative Modeling and Self-Supervised Learning for Text Generation
  • Generative Modeling with Diffusion Models for Image Generation
  • Deep Dive: Model Architectures
  • Pre-market Evaluation of Machine Learning Models
  • Deep Dive: Evaluation Metrics for Classification Models
  • Applications of AI in Diagnosis
  • Applications of AI to Improve Clinical Workflows
  • The Role of AI in Biomedical Research
  • Post-market Surveillance of Medical AI
  • Conversation: Regulatory Aspects of AI in Medicine

Earn a short course certificate of completion when you finish all coursework within 8 weeks.

Extend Your Learning and Save More

Whether you are building your medical science knowledge, preparing for advanced study, or expanding your understanding of the health care ecosystem, combining courses helps you connect concepts across disciplines while saving on enrollment.

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Enroll in Multiple Courses and Save

Combine up to five courses and unlock discounts up to 40%

You can bundle up to five HMX or HealthXcelerate courses. Bundle discounts apply to all courses selected. A bundle can consist of courses in the same upcoming enrollment period or the following enrollment period. 

Two-course bundle: 25% discount 
Three-course bundle: 30% discount 
Four-course bundle: 35% discount
Five-course bundle: 40% discount

View all bundle-eligible programs

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HMX and HealthXcelerate Learning Pass

Unlock both HMX and HealthXcelerate courses all year long with one simple enrollment fee

With the HMX and HealthXcelerate Learning Pass, a $4,950 investment unlocks one year of unlimited enrollment to Harvard Medical School's portfolio of HMX and HealthXcelerate online courses, empowering you to advance your knowledge of health care and medical science in the areas that matter most to your goals. Start Learning

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Learn as a Team with Group Enrollments

Expand your impact and savings when you learn together

HMX and HealthXcelerate courses are ideal for organizations looking to train teams or larger groups. Group pricing is available, making it a cost-effective investment in team development.

Contact Our Team

Group Enrollments

HMX courses are ideal for organizations looking to train teams or larger groups. Group pricing is available, making it a cost-effective investment in team development.

Faculty

HMX courses are led by Harvard Medical School faculty, working in collaboration with a multi-disciplinary team of experts in biomedical visualization, assessment, and the science of learning to create a unique learning experience that will stay with you.

David Blumenthal, MD, MPP
Professor of the Practice of Public Health and Health Policy, Harvard School of Public Health / 
Samuel O. Thier Professor of Medicine, Emeritus, Harvard Medical School

Michael J. Parker, MD
Assistant Professor of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center / Associate Dean for Online Learning Research, Harvard Medical School
 

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