Define the system
Frame the prediction task, intended use, workflow, and consequences of error.
Medical AI Technical Clarity
Build the model. Understand the evidence.
COURSE IN DEVELOPMENT · ENROLLMENT NOT OPEN
A planned self-paced course on developing and evaluating medical AI systems, with AI tools used throughout—and their output checked at every step.
Ask about the courseWHO IT’S FOR
Data scientists, ML engineers & software developers
Working Python knowledge, basic statistics, and familiarity with a simple ML model will help you get the most from the material.
HOW YOU’LL LEARN
Recorded lessons. Practical work. Your own pace.
Prerecorded videos, separate quizzes and assignments, flawed examples to investigate, and a cumulative educational project.
THE PLANNED LEARNING PATH
From a clinical question
to a defensible technical handoff.
Understand the data
Investigate provenance, labels, patient independence, signal quality, and leakage.
Build a reproducible baseline
Develop repeatable preprocessing, training, and experiment tracking.
Evaluate what matters
Choose metrics and thresholds, assess subgroups, and explain uncertainty.
Investigate failure
Study errors, distribution changes, and the experiments worth doing next.
Evaluate a generative AI feature
Test a bounded text task for correctness, omissions, unsupported claims, and unsafe behavior.
Integrate the model into software
Define inference behavior, input checks, versioning, integration, and monitoring.
Defend the evidence and next steps
Connect findings to a technical recommendation and explain which claims the evidence supports.
A NOTE ON THE FIRST EDITION
In production, with a clear direction.
The curriculum and first lesson draft are prepared. The full course and project are still being produced; pricing and a release date have not been announced. The first edition is planned as online-only and self-paced.
Projects are educational prototypes. Completing the course does not establish clinical readiness or regulatory clearance.
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