This research introduces the Alternative Annotator Test (AAT), a rigorous statistical method to determine when LLMs can reliably replace human annotators across domains.

• Establishes a formal benchmark for validating LLM-as-judge implementations
• Provides a statistical procedure that measures agreement and consistency between human and AI annotations
• Demonstrates the test's efficacy across multiple domains including summarization and math reasoning
• Offers practical guidance on when to trust AI evaluations versus human judgment

For medical applications, this framework provides a critical validation pathway for using AI to annotate clinical data, assess medical documentation, or evaluate healthcare communication—enabling more efficient research while maintaining scientific rigor.

The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs