Be at the forefront of the revolution of AI in Healthcare, and transform patient outcomes. Enable enhanced medical decision-making powered by machine learning to build the treatments of the future.
01Days06Hrs51Min03Sec
At 15 hours / week
Get access to the classroom immediately on enrollment
Learn to build, evaluate, and integrate predictive models that have the power to transform patient outcomes. Begin by classifying and segmenting 2D and 3D medical images to augment diagnosis and then move on to modeling patient outcomes with electronic health records to optimize clinical trial testing decisions. Finally, build an algorithm that uses data collected from wearable devices to estimate the wearer’s pulse rate in the presence of motion.
Intermediate Python, and Experience with Machine Learning
Learn the fundamental skills needed to work with 2D medical imaging data and how to use AI to derive clinically-relevant insights from data gathered via different types of 2D medical imaging such as x-ray, mammography, and digital pathology. Extract 2D images from DICOM files and apply the appropriate tools to perform exploratory data analysis on them. Build different AI models for different clinical scenarios that involve 2D images and learn how to position AI tools for regulatory approval.
Learn the fundamental skills needed to work with 3D medical imaging datasets and frame insights derived from the data in a clinically relevant context. Understand how these images are acquired, stored in clinical archives, and subsequently read and analyzed. Discover how clinicians use 3D medical images in practice and where AI holds most potential in their work with these images. Design and apply machine learning algorithms to solve the challenging problems in 3D medical imaging and how to integrate the algorithms into the clinical workflow.
Learn the fundamental skills to work with EHR data and build and evaluate compliant, interpretable models. You will cover EHR data privacy and security standards, how to analyze EHR data and avoid common challenges, and cover key industry code sets. By the end of the course, you will have the skills to analyze an EHR dataset, transform it to the right level, build powerful features with TensorFlow, and model the uncertainty and bias with TensorFlow Probability and Aequitas.
Learn how to build algorithms that process the data collected by wearable devices and surface insights about the wearer’s health. Cover the sensors and signal processing foundation that are critical for success in this domain, including IMU, PPG, and ECG that are common to most wearable devices, and learn how to build three algorithms from real-world sensor data.
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.
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.
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.
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.
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.
With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.
Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you, and keeping you on track.
You’ll have access to Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.
Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
We provide services customized for your needs at every step of your learning journey to ensure your success.
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Be at the forefront of the revolution of AI in Healthcare, and transform patient outcomes. Enable enhanced medical decision-making powered by machine learning to build the treatments of the future.
On average, successful students take 4 months to complete this program.
Artificial Intelligence has revolutionized many industries in the past decade, and healthcare is no exception. In fact, the amount of data in healthcare has grown 20x in the past 7 years, causing an expected surge in the Healthcare AI market from $2.1 to $36.1 billion by 2025 at an annual growth rate of 50.4%. AI in Healthcare is transforming the way patient care is delivered, and is impacting all aspects of the medical industry, including early detection, more accurate diagnosis, advanced treatment, health monitoring, robotics, training, research and much more.
By leveraging the power of AI, providers can deploy more precise, efficient, and impactful interventions at exactly the right moment in a patient’s care. In light of the worldwide COVID-19 pandemic, there has never been a better time to understand the possibilities of artificial intelligence within the healthcare industry and learn how you can make an impact to better the world’s healthcare infrastructure.
This program will help you apply your Data Science and Machine Learning expertise in roles including Physician Data Scientist; Healthcare Data Scientist; Healthcare Data Scientist, Machine Learning; Healthcare Machine Learning Engineer, Research Scientist, Machine Learning, and more roles in the healthcare and health tech industries that necessitate knowledge of AI and machine learning techniques.
If you are interested in applying your data science and machine learning experience in the healthcare industry, then this program is right for you. Additional job titles and backgrounds that could be helpful include Data Scientist, Machine Learning Engineer, AI Specialist, Deep Learning Research Engineer, and AI Scientist. This program is also a good fit for Researchers, Scientists, and Engineers who want to make an impact in the medical field.
There is no application. This Nanodegree program accepts everyone, regardless of experience and specific background.
To be best prepared to succeed in this program, students should be able to:
Intermediate Python:
Machine Learning:
To best prepare for this program, we recommend:
The AI for Healthcare Nanodegree program is comprised of content and curriculum to support four projects. Once you subscribe to a Nanodegree program, you will have access to the content and services for the length of time specified by your subscription. We estimate that students can complete the program in four months, working 15 hours per week. Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
You will have access to this Nanodegree program for as long as your subscription remains active. The estimated time to complete this program can be found on the webpage and in the syllabus, and is based on the average amount of time we project that it takes a student to complete the projects and coursework. See the Terms of Use and FAQs for other policies regarding the terms of access to our Nanodegree programs.
Please see the Udacity Program FAQs for policies on enrollment in our programs.
For this Nanodegree program, you will need a desktop or laptop computer running recent versions of Windows, Mac OS X, or Linux and an unmetered broadband Internet connection. For an ideal learning experience, a computer with Mac or Linux OS is recommended. You will use Python, PyTorch, TensorFlow, and Aequitas in this Nanodegree program.