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Glossary

Medical AI glossary

A plain-language dictionary of the terms that define the work of physicians who train and validate artificial intelligence. Each entry explains what it is, what it means for pay, and how to get started.

  • Medical data annotationThe expert process of labeling, structuring and verifying clinical information so an artificial intelligence can learn from it.
  • Clinical data labelingAssigning verified categories or values to health data (text, image, signal) so it can serve as an example to an AI model.
  • Medical RLHFReinforcement learning from human feedback (RLHF) applied to healthcare: physicians rank AI answers by quality to align the model.
  • Medical AI validationExpert verification that a model’s outputs are clinically correct, safe and useful before they are relied upon.
  • Medical AI trainerA healthcare professional who contributes their clinical judgment to teach, correct and evaluate artificial intelligence models in healthcare.
  • Medical AI reviewerA physician who critically examines an AI model’s answers and decides whether they are correct and safe to publish or use.
  • Double-blind consensusA method in which several physicians issue their verdict on a case without seeing the others’, and their agreement is aggregated into a robust conclusion.
  • Inter-rater agreementThe degree to which two or more independent raters agree when judging the same case, measured with statistics such as kappa or Krippendorff’s alpha.
  • Clinical ground truthThe answer taken to be correct — the gold standard — against which an AI model is trained and measured on a clinical task.
  • Medical AI hallucinationAn AI model answer that is factually false or invented but presented with the appearance of clinical certainty.
  • Clinical red teamingDeliberately putting a medical AI to the test with hard or tricky cases to uncover its failures before they reach a real user.
  • Annotated clinical caseA clinical case to which expert physicians have added labels, verified diagnoses or verdicts, ready to train or evaluate AI.
  • Medical AI fine-tuningRetraining an existing AI model with verified clinical data to specialize it in medical tasks.

Reviewed by the DataLaps editorial team. Last updated: 2026-07-11.

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