AI in Healthcare Capstone
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AI in Healthcare Capstone
This course is part of AI in Healthcare Specialization
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321 reviews
321 reviews
Skills you'll gain
- Machine Learning
- Artificial Intelligence and Machine Learning (AI/ML)
- Responsible AI
- Health Informatics
- Healthcare Ethics
- Patient-centered Care
- Model Optimization
- Data Collection
- Health Information Management
- Data Ethics
- Model Training
- Health Care Procedure and Regulation
- Model Evaluation
- Performance Tuning
- Artificial Intelligence
- Machine Learning Software
- Applied Machine Learning
- Fine-tuning
- Clinical Data Management
Tools you'll learn
Details to know
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 5 modules in this 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.
What's included
2 readings2 assignments2 peer reviews
2 readingsβ’Total 20 minutes
- Introductionβ’10 minutes
- Phase 1: Data Collectionβ’10 minutes
2 assignmentsβ’Total 60 minutes
- Phase 1. Project 1β’30 minutes
- Phase 1. Project 2β’30 minutes
2 peer reviewsβ’Total 60 minutes
- Phase 1 Peer Review - Project 1β’30 minutes
- Phase 1 Peer Review - Project 2β’30 minutes
What's included
1 reading2 assignments2 peer reviews
1 readingβ’Total 20 minutes
- Phase 2: Model Training, Part 1β’20 minutes
2 assignmentsβ’Total 60 minutes
- Phase 2. Project 1β’30 minutes
- Phase 2. Project 2β’30 minutes
2 peer reviewsβ’Total 60 minutes
- Phase 2 Peer Review - Project 1β’30 minutes
- Phase 2 Peer Review - Project 2β’30 minutes
What's included
1 reading2 assignments2 peer reviews
1 readingβ’Total 15 minutes
- Phase 3: Model Training, Part 2β’15 minutes
2 assignmentsβ’Total 60 minutes
- Phase 3. Project 1β’30 minutes
- Phase 3. Project 2β’30 minutes
2 peer reviewsβ’Total 60 minutes
- Phase 3 Peer Review - Project 1β’30 minutes
- Phase 3 Peer Review - Project 2β’30 minutes
What's included
1 reading2 assignments2 peer reviews
1 readingβ’Total 15 minutes
- Phase 4: Model Evaluationβ’15 minutes
2 assignmentsβ’Total 60 minutes
- Phase 4. Project 1β’30 minutes
- Phase 4. Project 2β’30 minutes
2 peer reviewsβ’Total 60 minutes
- Phase 4 Peer Review - Project 1β’30 minutes
- Phase 4 Peer Review - Project 2β’30 minutes
What's included
3 readings1 assignment1 peer review
3 readingsβ’Total 25 minutes
- Phase 5: Model Deployment and Regulationβ’5 minutes
- Wrap Upβ’10 minutes
- Claim CME Creditβ’10 minutes
1 assignmentβ’Total 30 minutes
- Phase 5β’30 minutes
1 peer reviewβ’Total 30 minutes
- Phase 5 Peer Reviewβ’30 minutes
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
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Reviewed on Jul 6, 2024
The quizzes have errors in the answers (already reported), and peer review is buggy. Received emails saying I have feedback but unable to see. Even AI grade and feedback cannot be seen.
Reviewed on Jul 14, 2025
Excellent course, easy to understand and flexible. Had great time learning new technique supported by AI and Machine Learning.
Reviewed on May 9, 2024
Capstone did a great job pulling together concepts from the earlier courses.
Frequently asked questions
Dates and Duration
Original Release Date: 11/16/2023
Expiration Date: 11/16/2026
Estimated Time to Complete: 11 hours
CME Credits Offered: 11.00
Accreditation
The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. The Stanford University School of Medicine designates this enduring material for a maximum of 11.00 AMA PRA Category 1 Creditsβ’. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Disclosures
The Stanford University School of Medicine adheres to ACCME Criteria, Standards and Policies regarding industry support of continuing medical education. There are no relevant financial relationships with ACCME-defined commercial interests for anyone who was in control of the content of this activity.
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
More questions
Financial aid available,
ΒΉ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.
