AI in Medicine: Biomedical Signal Interpretation

  • HMX
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Learn about advances in interpreting biomedical signals and the use of machine learning and deep learning to aid in analysis and diagnosis.
  • Online; Self-Paced

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

$495

Explore options to bundle courses and save.

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.

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Overview

Recent advances in AI are enabling automated systems to detect patterns in biomedical signals with human-level or even superhuman performance, expanding access to timely and accurate interpretation of signal data for clinical use. 

This HMX short course will introduce you to the key ideas and approaches behind AI interpretation of biomedical signals, highlighting techniques that can directly analyze data to uncover intricate patterns. This course offers a unique way for professionals to learn from leading experts about the key concepts and evolving impact of AI in biomedical signal interpretation. 

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Participant Types

Clinicians and health care professionals; researchers in biomedicine and industry sectors; and professionals in biotechnology or related 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 clinical professionals seeking a strong understanding of how machine learning can significantly enhance the analysis of biomedical signals and aid in predicting patient responses to treatment; researchers in biomedicine and industry sectors who are interested in learning how AI can be used to collect data and analyze biomedical signals; and professionals in biotechnology who are eager to learn how AI could be incorporated into the next generation of biomedical signal interpretation technologies.
 

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.

Frequently Asked Questions

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  • Detecting Arrhythmias Using Deep Learning
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  • Assessing the Potential of Deep Learning for Detection of Arrhythmias
  • Predicting Psychiatric Disorder Symptoms with EEG and Machine Learning
  • Evaluating the Predictive Capacity of the EEG Model
  • In Practice: Evolving Applications of AI in Biomedical Signal Interpretation

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

Are you ready to elevate your educational journey and maximize the investment in your future?

You can bundle up to five HMX short courses. Bundle discounts apply to all courses selected. All courses in a bundle must be completed during the following two enrollment periods.

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

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.

Contact Our Team

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.
Pranav Rajpurkar headshot.

Pranav Rajpurkar

PhD

  • Assistant Professor of Biomedical Informatics, Harvard Medical School

David Kim, MD, PhD
Assistant Professor of Emergency Medicine, Stanford University

Michael 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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