Assessing the accuracy and readability of ChatGPT-4 and Gemini in answering oral cancer queries—an exploratory study

Abstract

Aim: This study aims to evaluate the accuracy and readability of responses generated by two large language models (LLMs) (ChatGPT-4 and Gemini) to frequently asked questions by lay persons (the general public) about signs and symptoms, risk factors, screening, diagnosis, treatment, prevention, and survival in relation to oral cancer. Methods: The accuracy of each response given in the two LLMs was rated by four oral cancer experts, blinded to the source of the responses. The accuracy was rated as 1: complete, 2: correct but insufficient, 3: includes correct and incorrect/outdated information, and 4: completely incorrect. Frequency, mean scores for each question, and overall were calculated. Readability was analyzed using the Flesch Reading Ease and the Flesch-Kincaid Grade Level (FKGL) tests. Results: The mean accuracy scores for ChatGPT-4 responses ranged from 1.00 to 2.00, with an overall mean score of 1.50 (SD 0.36), indicating that responses were usually correct but sometimes insufficient. Gemini responses had mean scores ranging from 1.00 to 1.75, with an overall mean score of 1.20 (SD 0.27), suggesting more complete responses. The Mann-Whitney U test revealed a statistically significant difference between the models’ scores (p = 0.02), with Gemini outperforming ChatGPT-4 in terms of completeness and accuracy. ChatGPT generally produces content at a lower grade level (average FKGL: 10.3) compared to Gemini (average FKGL: 12.3) (p = 0.004). Conclusions: Gemini provides more complete and accurate responses to questions about oral cancer that lay people may seek answers to compared to ChatGPT-4, although its responses were less readable. Further improvements in model training and evaluation consistency are needed to enhance the reliability and utility of LLMs in healthcare settings.

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