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Evidence deployedHe LabPackage 0.3.0

Methodological sensitivity of healthcare AI benchmarks

A review and controlled pilot asking how temperature, repeat count, reporting, graders, and inter-rater reliability change the meaning of healthcare AI benchmark results.

Study state
Evidence review ready
Protocol
0.2.0
Model calls
Disabled
Public DOI
None
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Check the research background

The background work is ready to inspect

The package combines reporting standards, benchmark papers, methods studies, and a source-level audit. It supports choosing the first detailed method review without starting the literature search again.

Background conclusion

A healthcare benchmark should report its served configuration and distinguish average performance, repeated-output stability, grader agreement, and clinical validity. One score cannot stand in for all four.

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