Quality Of Answers Of Generative Large Language Models Vs Peer Patients For Interpreting Lab Test Results For Lay Patients: Evaluation Study
He Zhe, Bhasuran Balu, Jin Qiao, Tian Shubo, Hanna Karim, Shavor Cindy, Arguello Lisbeth Garcia, Murray Patrick, Lu Zhiyong. Arxiv 2024
[Paper]
Lab results are often confusing and hard to understand. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to get their questions answered. We aim to assess the feasibility of using LLMs to generate relevant, accurate, helpful, and unharmful responses to lab test-related questions asked by patients and to identify potential issues that can be mitigated with augmentation approaches. We first collected lab test results related question and answer data from Yahoo! Answers and selected 53 QA pairs for this study. Using the LangChain framework and ChatGPT web portal, we generated responses to the 53 questions from four LLMs including GPT-4, Meta LLaMA 2, MedAlpaca, and ORCA_mini. We first assessed the similarity of their answers using standard QA similarity-based evaluation metrics including ROUGE, BLEU, METEOR, BERTScore. We also utilized an LLM-based evaluator to judge whether a target model has higher quality in terms of relevance, correctness, helpfulness, and safety than the baseline model. Finally, we performed a manual evaluation with medical experts for all the responses to seven selected questions on the same four aspects. The results of Win Rate and medical expert evaluation both showed that GPT-4’s responses achieved better scores than all the other LLM responses and human responses on all four aspects (relevance, correctness, helpfulness, and safety). However, LLM responses occasionally also suffer from a lack of interpretation in one’s medical context, incorrect statements, and lack of references. We find that compared to other three LLMs and human answer from the Q&A website, GPT-4’s responses are more accurate, helpful, relevant, and safer. However, there are cases which GPT-4 responses are inaccurate and not individualized. We identified a number of ways to improve the quality of LLM responses.
Similar Work