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AI for Healthcare

Nanodegree Program

The AI for Healthcare program offers two courses that apply AI to 2D and 3D medical imaging data. The first course covers fundamental skills needed to work with 2D imaging data, such as extracting images from DICOM files, building AI models, and obtaining regulatory approval. The course project involves training a CNN to classify chest X-rays for the presence of pneumonia and writing an FDA validation plan. The second course covers 3D imaging data, including clinical fundamentals, imaging modalities, and common analysis tasks. It also explores how AI can be integrated into the clinical workflow. Both courses are designed to teach students how to derive clinically relevant insights from medical imaging data using AI.

The AI for Healthcare program offers two courses that apply AI to 2D and 3D medical imaging data. The first course covers fundamental skills needed to work with 2D imaging data, such as extracting images from DICOM files, building AI models, and obtaining regulatory approval. The course project involves training a CNN to classify chest X-rays for the presence of pneumonia and writing an FDA validation plan. The second course covers 3D imaging data, including clinical fundamentals, imaging modalities, and common analysis tasks. It also explores how AI can be integrated into the clinical workflow. Both courses are designed to teach students how to derive clinically relevant insights from medical imaging data using AI.

Advanced

3 months

Real-world Projects

Completion Certificate

Last Updated March 21, 2023

Skills you'll learn:
Feature engineering • Medical and healthcare regulations and standards • Medical imaging basics • Fda medical device framework
Prerequisites:
Data cleaning • Machine learning frameworks in Python • Basic supervised machine learning

Courses In This Program

Course 1 30 minutes

Welcome to the AI for Healthcare Nanodegree Program

Course 2 3 weeks

Applying AI to 2D Medical Imaging Data

Course 3 3 weeks

Applying AI to 3D Medical Imaging Data

Course 4 3 weeks

Applying AI to EHR Data

With the transition to electronic health records (EHR) over the last decade, the amount of EHR data has increased exponentially, providing an incredible opportunity to unlock this data with AI to benefit the healthcare system. Learn the fundamental skills of working with EHR data in order to build and evaluate compliant, interpretable machine learning models that account for bias and uncertainty using cutting-edge libraries and tools including Tensorflow Probability, Aequitas, and Shapley. Understand the implications of key data privacy and security standards in healthcare. Apply industry code sets, transform datasets at different EHR data levels, and use Tensorflow to engineer features.

Taught By The Best

Photo of Michael DAndrea

Michael DAndrea

Principal Data Scientist at Genentech

Michael is on the Pharma Development Informatics team at Genentech (part of the Roche Group), where he works on improving clinical trials and developing safer, personalized treatments with clinical and EHR data. Previously, he was a Lead Data Scientist on the AI team at McKesson's Change Healthcare.

Photo of Ivan Tarapov

Ivan Tarapov

Sr. Program Manager at Microsoft Research

At Microsoft Research, Ivan works on robust auto-segmentation algorithms for MRI and CT images. He has worked with Physio-Control, Stryker, Medtronic, and Abbott, where he has helped develop external and internal cardiac defibrillators, insulin pumps, telemedicine, and medical imaging systems.

Photo of Mazen Zawaideh

Mazen Zawaideh

Radiologist

Mazen Zawaideh is a Neuroradiology Fellow at the University of Washington, where he focuses on advanced diagnostic imaging and minimally invasive therapeutics. He also served as a Radiology Consultant for Microsoft Research for AI applications in oncologic imaging.

Photo of Nikhil Bikhchandani

Nikhil Bikhchandani

Data Scientist at Verily Life Sciences

Nikhil Bikhchandani spent five years working with wearable devices at Google and Verily Life Sciences. His work with wearables spans many domains including cardiovascular disease, neurodegenerative diseases, and diabetes. Before Alphabet, he earned a B.S. and M.S. in EE and CS at Carnegie Mellon.

Photo of Emily Lindemer

Emily Lindemer

Director of Data Science & Analytics at Wellframe

Emily is an expert in AI for both medical imaging and translational digital healthcare. She holds a PhD from Harvard-MIT's Health Sciences & Technology division and founded her own digital health company in the opioid space. She now runs the data science division of Wellframe.

Ratings & Reviews

Average Rating: 4.6 Stars

(126 Reviews)

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