There has been some existing work assessing LLM-as-a-Judge for medical fields. 
\citet{szymanski2024limitationsllmasajudgeapproachevaluating} found relatively low LLM-expert agreement (60-64\%) in medical domains compared to expert-expert agreement (72-75\%), while LLM-layperson agreement reached 80\%, suggesting expert ``personas" may worsen performance. For medical safety evaluation, \citet{han2024medsafetybenchevaluatingimprovingmedical} introduced MedSafetyBench, finding medical LLMs complied with harmful requests more frequently than general LLMs.
\citet{kanithi2024mediccomprehensiveframeworkevaluating} proposed MEDIC, using three LLM judges to evaluate clinical applications across five dimensions, finding high judge alignment (up to 78.23\%) with Prometheus showing strong correlations with clinician evaluations. \citet{krolik2024leveraginglargelanguagemodels} used ChatGPT-4o to evaluate medical Q\&A on metrics including relevance, succinctness, medical correctness, hallucination, and coherence.
\citet{zheng2023judgingllmasajudgemtbenchchatbot} found GPT-4 and human agreement reached 86\%, exceeding human-human agreement (81\%), suggesting LLM-as-a-Judge could become a new evaluation standard. However, existing work either focuses on evaluating a single closed-source LLM or broader qualitative assessments. Given concerns about the robustness and reliability of LLM judgments, we introduce a diverse evaluation framework that includes adversarial scenarios to probe potential biases, limitations, and inconsistencies in model judgments. Additional related work details are provided in Appendix \ref{sec:long_related_work}. 

% \textcolor{red}{We should put 1 sentence to prompt why we wanted diversity of evaluation and want an adversarial learning part. Can you summarize why we thought it was useful in related work?}