Towards Leveraging Large Language Models for Automated Medical Q&A Evaluation
This addresses the costly and time-consuming manual evaluation by medical professionals, but it is incremental as it builds on existing LLM capabilities for a specific domain.
This paper tackled the problem of automating medical Q&A evaluation by exploring if large language models can reliably replicate human assessments, finding promising results that could save time for medical experts.
This paper explores the potential of using Large Language Models (LLMs) to automate the evaluation of responses in medical Question and Answer (Q\&A) systems, a crucial form of Natural Language Processing. Traditionally, human evaluation has been indispensable for assessing the quality of these responses. However, manual evaluation by medical professionals is time-consuming and costly. Our study examines whether LLMs can reliably replicate human evaluations by using questions derived from patient data, thereby saving valuable time for medical experts. While the findings suggest promising results, further research is needed to address more specific or complex questions that were beyond the scope of this initial investigation.