Medical imaging AI: how it is built and checked
Understand how imaging models are trained, validated and overseen, for education only
A taste of a lesson
A paper says a model has 95 percent sensitivity and 95 percent specificity. Does that mean 95 percent of its alerts are correct?
No, that share is the positive predictive value, and it depends on prevalence. Take 10,000 people with 1 percent prevalence: 100 have the condition, and 95 are flagged. Of the 9,900 without it, 5 percent, or 495, are flagged too. So 95 of 590 alerts are true, about 16 percent. At 20 percent prevalence the picture changes a lot. That is why evaluation must match the setting where a model would be used, decided with clinicians. Quick exercise: compute the PPV for the same model at 20 percent prevalence.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain how expert labels are collected and why disagreement matters
- Split imaging data by patient and explain the leakage it prevents
- Calculate positive predictive value from sensitivity, specificity and prevalence
- Describe external validation, shortcut learning and subgroup evaluation
- Outline oversight, regulatory and privacy considerations in broad terms
Lesson plan
- 1 Tasks and data in medical imaging Describe common imaging tasks and the kinds of images and data involved. Start
- 2 Labels and expert disagreement Understand where labels come from and how disagreement affects training and testing. Start
- 3 Splits and metrics that matter Split by patient and compute sensitivity, specificity and PPV correctly. Start
- 4 External validation and shortcut learning See why models must be tested at new sites and how shortcuts mislead. Start
- 5 Subgroups and human oversight Evaluate fairness across groups and design for appropriate human review. Start
- 6 Regulation, privacy and governance in broad terms Know which questions to ask about approval, privacy and monitoring. Start
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About this tutor
For students, engineers, health administrators and curious clinicians who want to understand how AI for medical images is built and evaluated. This is education only: the tutor never interprets images or gives clinical advice and always defers to clinicians, regulators and local rules. You will learn how expert labels are collected and why experts disagree, why data must be split by patient, how sensitivity, specificity and prevalence combine into the numbers that matter in practice, and why models must be validated across hospitals and scanners. You also study shortcut learning, subgroup performance, the existence of regulatory approval processes that differ by country, human oversight and health data privacy.
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About the teacher
MLOps without the ceremony: tracking, versioning, monitoring and responsible deployment
9 tutors 322 lessons taught Sample
I teach the habits that keep machine learning systems trustworthy after the notebook: tracking experiments, versioning data and models, testing, monitoring, handling incidents and documenting models honestly. I came to this from software operations, where I learned that most failures are boring and preventable, and then spent years helping small teams put models into production without drowning in tooling. I...
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