Program profile · official sources
Applications of Machine Learning in Medicine Program
Stanford Online offers this two-course programme through Stanford School of Medicine and Stanford Medicine Health Education. It teaches healthcare data foundations, model development, deployment and ethical considerations. Topics include clinical text, electronic phenotyping, time-series data, predictive models and deep learning. The self-paced programme addresses clinicians, researchers and data professionals; coding experience is helpful but not necessary. Successful completion of both courses earns a blockchain-verified Stanford Certificate of Achievement. Individual-course and All-Access enrollment routes have different access periods.
Facts checked 15 Oct 2026
- Price
- USD 1425
- As shown in the United States on 15 October 2026, All-Access costs USD 1,425 with a full year for both courses and 365 days of teaching-assistant email access. Individual courses cost USD 715 each with 60 days to complete and 60 days of teaching-assistant email access. The page calls All-Access a one-year subscription, but does not establish automatic renewal or a recurring charge.
- Provider link
See price and enroll at Stanford Online (official site) ↗
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- Time
- Headline: 10 hours per course and 20 hours for the certificate; credential text separately states a minimum of 60 hours
- Level
- Not listed
- Credential
- Stanford Certificate of Achievement in Applications of Machine Learning in Medicine
Shareable on LinkedIn or other social platforms and verifiable by employers.
- Prerequisites
- A coding background is helpful but not necessary. The page does not establish other formal academic admission requirements. As checked on 15 October 2026, the embedded descriptions of both required courses, XMLPH110 and XMLPH210, require R/R Studio to complete homework assignments. Download instructions are included, and R/R Studio is free to download and use. This is a course-tool requirement, not a prior-coding admission requirement.
- Format
- 100% online, self-paced and on-demand
Commercial status: no commercial relationship is documented for this page. The verification link goes directly to the official provider source.
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What’s covered
- Data Foundations for Machine Learning in Medicine
- Model Development, Deployment, and Ethical Considerations
Prerequisites: A coding background is helpful but not necessary. The page does not establish other formal academic admission requirements. As checked on 15 October 2026, the embedded descriptions of both required courses, XMLPH110 and XMLPH210, require R/R Studio to complete homework assignments. Download instructions are included, and R/R Studio is free to download and use. This is a course-tool requirement, not a prior-coding admission requirement.
What you actually do
What the credential is
- Credential type
- Stanford Certificate of Achievement in Applications of Machine Learning in Medicine · online.stanford.edu / applications-machine-learning-medicine-program
- Credential
- Stanford Certificate of Achievement in Applications of Machine Learning in Medicine · online.stanford.edu / applications-machine-learning-medicine-program
- Issuer
- Stanford University · online.stanford.edu / applications-machine-learning-medicine-program
- Sharing
- Shareable on LinkedIn or other social platforms and verifiable by employers. · online.stanford.edu / applications-machine-learning-medicine-program
- Exam requirement
- Learning-outcome assessment is described, but the precise final assessment format and passing threshold are not established. · online.stanford.edu / applications-machine-learning-medicine-program
- Credential scope
- As checked on 15 October 2026, completing each of the two required courses earns the named blockchain-verified Stanford Certificate of Achievement. The credential section mentions learning-outcome assessments and other School of Engineering criteria, but lists projects, essays, presentations and proctored exams as possible forms rather than confirming one required exam. The visible programme overview states Stanford Medicine's joint continuing-education accreditation without a numeric amount. As checked on 15 October 2026, the embedded descriptions for XMLPH110: Data Foundations for Machine Learning in Medicine and XMLPH210: Model Development, Deployment, and Ethical Considerations each designate a maximum of 10.00 AMA PRA Category 1 Credits. Physicians should claim only credit commensurate with their participation in each activity. These course-specific maxima do not establish an automatic 20-credit programme award or academic degree credit; linked full accreditation pages were not read. · online.stanford.edu / applications-machine-learning-medicine-program
Alternatives in the registry
Related by topic or level.
- DeepLearning.AI TensorFlow Developer Professional Certificate : USD 49 (The US course FAQ states $49/month for access to all four courses until the subscription ends. It separately describes a one-time $49 single-course payment with access for 180 days or until completion. These are different payment scopes, not a total completion cost.) (price checked 29 September 2026); Overview: 2 months at 10 hours a week. These are provider estimates; no combined completion promise is inferred. (duration checked 29 September 2026).
- AI Fundamentals : DataCamp Premium : USD 35/month for course content, billed monthly at the regular price (price checked 8 September 2026); Approximately 9 hours (duration checked 8 September 2026).
- Artificial Intelligence Graduate Certificate (Online) : USD 14320 (As shown in the United States on 5 October 2026, the official certificate page displays Tuition $14,320 in its header beside 4 online courses. The cost-of-attendance page it links to was not captured, so per-course tuition and other fees are not established. Taxes are not stated.); Finish in 8 months to 3 years (provider-listed); most students complete within 18 to 24 months (duration checked 5 October 2026).
Change log
- : Initial source review from the dated US capture. · Source
See price and enroll at Stanford Online (official site) ↗
Official site. No commission.