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\title{LLMs as Medical Safety Judges: Evaluating Alignment with Human Annotation in Patient-Facing QA}

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\author{
  \textbf{Yella Leonie Diekmann\textsuperscript{1}},
  \textbf{Chase M. Fensore\textsuperscript{1}},
  \textbf{Rodrigo M. Carrillo-Larco\textsuperscript{2}}, \\
  \textbf{Eduard R. Castejon Rosales\textsuperscript{3}}, 
  \textbf{Sakshi Shiromani\textsuperscript{4}},
  \textbf{Rima Pai\textsuperscript{2}}, \\
  \textbf{Megha Shah\textsuperscript{3}},
  \textbf{Joyce C. Ho\textsuperscript{1}} \\
  \textsuperscript{1}Department of Computer Science, Emory University \\
  \textsuperscript{2}Rollins School of Public Health, Emory University \\
%  \textsuperscript{3}Universidad Peruana Cayetano Heredia \\
  \textsuperscript{3}Department of Family and Preventive Medicine, Emory School of Medicine \\
  \textsuperscript{4}Department of Ophthalmology, Emory University School of Medicine \\
  \texttt{yella.diekmann@emory.edu} \\
}



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\begin{document}
\maketitle
\begin{abstract}
The increasing deployment of LLMs in patient-facing medical QA raises concerns about the reliability and safety of their responses. Traditional evaluation methods rely on expert human annotation, which is costly, time-consuming, and difficult to scale. This study explores the feasibility of using LLMs as automated judges for medical QA evaluation. We benchmark LLMs against human annotators across eight qualitative safety metrics and introduce adversarial question augmentation to assess LLMs' robustness in evaluating medical responses. Our findings reveal that while LLMs achieve high accuracy in objective metrics such as scientific consensus and grammaticality, they struggle with more subjective categories like empathy and extent of harm. This work contributes to the ongoing discussion on automating safety assessments in medical AI and informs the development of more reliable evaluation methodologies.
\end{abstract}

\section{Introduction}
\input{introduction}

\section{Related Work}
\input{short_related_work}

\section{Methodology}
\input{methodology}

\section{Results}
\input{Results}

\section{Discussion}
\input{Discussion}

\section{Acknowledgments}
This work was funded in part by the National Science Foundation (NSF) grant IIS-2145411 and the National Institute on Minority Health and Health Disparities (NIMHD) under grants K23 MD015088 and R01 MD018528. 


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\appendix
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\input{latex/appendix_A}
\input{latex/appendix_B}

\end{document}
