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Dr. Ram KumarDiabetes & metabolic health

Medical AI · Track 8 of 10

Evaluation, safety & deployment

What happens after the model works on your test set: external validation, dataset shift, bias, reporting standards, red-teaming and monitoring.

For anyone who builds, buys or approves clinical AI.

By the end of this track you should be able to

  • Plan an external validation and spot dataset shift
  • Measure and report performance across patient subgroups
  • Use TRIPOD+AI, CONSORT-AI, SPIRIT-AI, DECIDE-AI and CLAIM
  • Red-team an LLM application and monitor a model after deployment

Lessons

  1. External validation and dataset shift

    Coming soon

  2. Bias and fairness

    Coming soon

  3. Reporting guidelines: TRIPOD+AI, CONSORT-AI, SPIRIT-AI, DECIDE-AI, CLAIM

    Coming soon

  4. Red-teaming LLM applications

    Coming soon

  5. Monitoring and MLOps

    Coming soon

Lessons in this track are being written and reviewed. In the meantime, start with the Foundations track.

Next track: Regulation, ethics & governance

Educational content only. This material teaches methods; it is not clinical advice. Code, models and examples here are not validated medical devices and must not be used to make decisions about real patients without proper clinical validation, governance and any required regulatory approval. Examples use synthetic or openly licensed de-identified data only. Never paste identifiable patient information into public AI tools. Mentions of models, tools or vendors are for identification, not endorsement.