Comparative Accuracy of ChatGPT 4.0 and Google Gemini in Answering Pediatric Radiology Text-Based Questions

Abstract

AIMS AND OBJECTIVES: This study evaluates the accuracy of two AI language models, ChatGPT 4.0 and Google Gemini (as of August 2024), in answering a set of 79 text-based pediatric radiology questions from "Pediatric Imaging: A Core Review." Accurate interpretation of text and images is critical in radiology, making AI tools valuable in medical education. METHODS: The study involved 79 questions selected from a pediatric radiology question set, focusing solely on text-based questions. ChatGPT 4.0 and Google Gemini answered these questions, and their responses were evaluated using a binary scoring system. Statistical analyses, including chi-square tests and relative risk (RR) calculations, were performed to compare the overall and subsection accuracy of the models. RESULTS: ChatGPT 4.0 demonstrated superior accuracy, correctly answering 83.5% (66/79) of the questions, compared to Google Gemini's 68.4% (54/79), with a statistically significant difference (p=0.0255, RR=1.221). No statistically significant differences were found between the models within individual subsections, with p-values ranging from 0.136 to 1. CONCLUSION: ChatGPT 4.0 outperformed Google Gemini in overall accuracy for text-based pediatric radiology questions, highlighting its potential utility in medical education. However, the lack of significant differences within subsections and the exclusion of image-based questions underscore the need for further research with larger sample sizes and multimodal inputs to fully assess AI models' capabilities in radiology.

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