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Swali 1 ya 8

A model ranks compounds for laboratory testing. What must be specified before interpreting its performance?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. A model ranks compounds for laboratory testing. What must be specified before interpreting its performance?
    • Only the model family name
    • The number of dashboard charts
    • The final commercial price
    • Intended use, population, endpoint, and decision boundary
  2. Why is retrospective performance insufficient by itself for a clinical or regulatory claim?
    • Historical data are never useful
    • The validation may not match prospective context, population, or information timing
    • Retrospective models cannot be evaluated
    • A large test set guarantees approval
  3. A biomarker model is evaluated on records from the same sites used for training. What transfer question remains?
    • Whether it generalizes to a new study site or data conditions
    • Whether the training loss reached zero
    • Whether it has a polished interface
    • Whether the feature names are alphabetized
  4. Why inspect batch effects and missing measurements?
    • They only affect storage costs
    • Missing data always improve generalization
    • They can distort model inputs or create apparent patterns unrelated to the target
    • Batch labels are equivalent to outcomes
  5. What does the guide say FDA/EMA AI principles do?
    • Approve every model that follows a checklist
    • Offer a framework for evidence and accountability, not blanket model approval
    • Replace context-of-use validation
    • Guarantee a regulatory submission succeeds
  6. A model is used to prioritize exploratory lab work. How should that result be described?
    • As proof of clinical effectiveness
    • As a validated patient outcome
    • As a regulatory decision by itself
    • As an exploratory hypothesis unless evidence supports a stronger claim
  7. When assay data or model versions change, what should the team do?
    • Assume the prior validation remains unchanged
    • Delete the previous protocol
    • Use the documented reassessment trigger and evaluate the new setup
    • Keep the old version in reporting only
  8. Why retain protocols, model outputs, and review decisions?
    • They support traceability and later review of the evidence and decisions
    • They guarantee the model is unbiased
    • They eliminate monitoring
    • They are substitutes for data provenance