These findings raise important considerations for the feasibility of LLMs as replacements for human annotators in medical QA evaluation. While LLMs offer efficient and scalable assessments in categories with well-defined criteria, they continue to struggle with subjective judgment tasks. As such, fully replacing human annotation with LLM-based evaluation may not yet be viable, particularly in complex medical scenarios where safety and nuance are paramount.

A hybrid evaluation framework may offer a practical alternative. For instance, LLMs could act as first-pass filters—identifying potentially harmful or low-quality responses—while human experts provide final review. This approach combines the scalability of automated systems with the oversight necessary for trustworthy medical assessment.

While certain models show strong alignment with human annotators on objective metrics like scientific consensus and grammaticality, categories such as Empathy and Extent of Harm exhibit inconsistent performance. 
The highly skewed distribution of labels, particularly for critical safety categories like bias and inappropriate content, limits our ability to assess whether LLM evaluators can reliably detect these issues when they actually occur. Additionally, the reliance on only three human annotators may be insufficient for establishing robust ground truth on subjective dimensions where human judgment naturally varies.
We also observed that some models may overestimate potential harm, while others err on the side of underestimation—an important consideration in patient safety contexts. 
 Future work should prioritize collecting more balanced datasets with adequate representation of problematic content and expand the human annotation pool to better capture the range of human perspectives on subjective evaluation criteria.
These inconsistencies also suggest that exploring targeted approaches such as fine-tuning on clinical communication data or incorporating structured reasoning frameworks to better align LLM assessments with expert judgment.

Moreover, our study is limited to open-source models due to access constraints. Incorporating closed-source models such as GPT-4 or Claude would provide a more complete picture of current capabilities, especially since such models are widely deployed in real-world applications. Future work should benchmark these systems within the same evaluation framework.

Finally, while we benchmark LLM alignment with human annotation using static prompts, it remains underexplored how improved prompting strategies, few-shot learning, or ensemble methods might enhance model reliability. Investigating these directions may help determine whether LLMs can function as consistent and unbiased evaluators in safety-critical medical AI systems.