A Quick Review of the Stamford AI in Healthcare Courses (AI in Healthcare Specialization) Capstone Project.

For some, the capstone standalone may be equally as valuable as the course.

Photo by Barna Bartis on Unsplash

In my prior post about the Stamford AI in Healthcare Course, I covered the course components, but held off on the actual Capstone Project. This is a much shorter article about the benefits and potential to take on solely the Capstone project as a review of the challenges of AI on Healthcare.

For the most part the Capstone Project leverages the third course on Fundamentals of AI and Machine Learning for Healthcare and the fourth course on Evaluations of AI Applications in Healthcare. The first two courses on Introduction to Healthcare and Introduction to Clinical Data are not key to understanding the challenges presented by the Capstone. The assignments while peer-reviewed seem to be largely treated as self-review given the large numbers of duplicate cut and paste answers.

The goals of the Capstone are to understand the challenges with using x-rays and neural networks to predict intubation as well as using columnar data to make other predictions as well as understanding bias.

If you are short on time, the Coursera sequence allows it and you have a reasonable background in machine learning, you may find that the Capstone project itself provides a reasonable overview of the challenges in implementing AI in Healthcare particularly the environmental and timing/implementation challenges in acting on model predictions.

If on the other hand, you want to better understand the overall healthcare ecosystem and the types of data and data sources, courses one and two provide that learning opportunity and skipping the Capstone project would not be an issue.

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