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Towards Leveraging Large Language Models for Automated Medical Q&A Evaluation

Jack Krolik, Herprit Mahal, Feroz Ahmad, Gaurav Trivedi, Bahador Saket

TL;DR

This study examines whether LLMs can reliably replicate human evaluations by using questions derived from patient data, thereby saving valuable time for medical experts.

Abstract

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.

Towards Leveraging Large Language Models for Automated Medical Q&A Evaluation

TL;DR

This study examines whether LLMs can reliably replicate human evaluations by using questions derived from patient data, thereby saving valuable time for medical experts.

Abstract

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.
Paper Structure (19 sections, 2 figures, 3 tables)

This paper contains 19 sections, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Dataset for Evaluation
  3. Data Anonymization
  4. Data Collection Methodology
  5. Metrics for Response Evaluation
  6. Automating the Evaluation
  7. Qualitative Review of Automated Medial Q&A Evaluation
  8. Prompt Improvement
  9. Discussion and Future Work
  10. Time Efficiency and Resource Allocation
  11. LLM as a Complementary Evaluation Tool
  12. Potential Areas for Improvement
  13. Multi-Model Approach
  14. Iterative Prompt Engineering
  15. Study Limitations and Ethical Considerations
  16. ...and 4 more sections

Figures (2)

  • Figure 1: Initial prompt used for automating the evaluation process. It includes: The image shows an automated evaluation prompt used to guide an LLM in assessing Q&A system responses. It includes the Assessment Set which is a collection of questions, ground truth answers, and system responses, Evaluation Metrics which are definitions and criteria for the unique metrics to ensure accurate assessments, and the Evaluation Format that covers instructions for structuring responses as a JSON object, enabling consistent integration with existing evaluation processes.
  • Figure 2: Case Study: Comparison of actual and LLM-recommended asthma treatments. Correct recommendations are highlighted in yellow, incorrect in red. The medical team gave perfect scores, while the LLM identified specific issues in medical correctness, hallucination, and completeness, showcasing its potential in medical recommendations.