Chevron Left
Back to AI in Healthcare Capstone

Learner Reviews & Feedback for AI in Healthcare Capstone by Stanford University

4.7
stars
313 ratings

About the Course

This capstone project takes you on a guided tour exploring all the concepts we have covered in the different classes up till now. We have organized this experience around the journey of a patient who develops some respiratory symptoms and given the concerns around COVID19 seeks care with a primary care provider. We will follow the patient's journey from the lens of the data that are created at each encounter, which will bring us to a unique de-identified dataset created specially for this specialization. The data set spans EHR as well as image data and using this dataset, we will build models that enable risk-stratification decisions for our patient. We will review how the different choices you make -- such as those around feature construction, the data types to use, how the model evaluation is set up and how you handle the patient timeline -- affect the care that would be recommended by the model. During this exploration, we will also discuss the regulatory as well as ethical issues that come up as we attempt to use AI to help us make better care decisions for our patient. This course will be a hands-on experience in the day of a medical data miner. In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content....

Top reviews

NR

Jul 14, 2025

Excellent course, easy to understand and flexible. Had great time learning new technique supported by AI and Machine Learning.

DR

May 9, 2024

Capstone did a great job pulling together concepts from the earlier courses.

Filter by:

51 - 60 of 60 Reviews for AI in Healthcare Capstone

By shihyang y

•

Feb 25, 2024

it is very good

By Atul P

•

May 7, 2021

Great team !!

By Artem S

•

Mar 11, 2024

Really great

By Chandràkanta M

•

Aug 24, 2023

great course

By Stefany V

•

Jan 6, 2024

muito bom

By 권광현

•

Apr 14, 2024

도움되었어요

By Dr P

•

Apr 1, 2026

The AI in Healthcare Specialization provides a clear and practical introduction to the role of artificial intelligence in modern clinical practice. The course effectively covers key areas such as clinical data, fundamentals of machine learning, and evaluation of AI applications in healthcare. What I found most valuable was the focus on real-world clinical relevance, including how AI can support decision-making, improve patient outcomes, and be safely integrated into healthcare systems. The emphasis on ethical considerations and limitations of AI is particularly important for clinicians.

By Joshua A C

•

Oct 15, 2020

I would have liked to see more hands on with users actually writing code in a notebook. The quizzes need to be verified because some answers may not be correct.

By Mansij B (

•

Aug 10, 2025

Helpful

By Neil B

•

Mar 28, 2025

Some excellent material in this course. However for a Stanford level course I expected better. They were typos scattered throughout each of the courses. For an AI course that talks about accessing text data and analysing why can’t you run an AI engine over this to fine and correct typos? I was very surprised in one of the courses for a Stanford professor to incorrectly state the primary colours as red, yellow and green. Green is not a primary colour. Blue is. Then I had a very poor experience at the end where a bug in the Software Incorrectly graded 2 of the assignments as 200% and 300% preventing me receiving my certificate. This went on for weeks with multiple emails and chats and incorrect information from your staff who did not tell me I could just retake it which I did and fixed it.