ArticleCureus2024
Comparative Accuracy of ChatGPT 4.0 and Google Gemini in Answering Pediatric Radiology Text-Based Questions.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
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- Evaluating the clinical decision-making performance of large language models in clinically oriented thoracic anatomy scenarios: a comparative evaluation study.BMC medical education · 2026Article
- Evaluating the performance of five large language models in generating patient educational content for pediatric cardiothoracic procedures: a comparative study.General thoracic and cardiovascular surgery · 2026Observational
- Evaluation of AI language models in answering pregnancy-related questions assessed by obstetrics specialists.Scientific reports · 2026Article
- AI vs. MD: Benchmarking ChatGPT and Gemini for Complex Wound Management.Journal of clinical medicine · 2025Article
- Comparison of the accuracy and reliability of ChatGPT-4o and Gemini in answering HIV-related questions.BMC infectious diseases · 2025Article
- Evaluating the performance of five large language models in answering Delphi consensus questions relating to patellar instability and medial patellofemoral ligament reconstruction.BMC musculoskeletal disorders · 2025Article
- Layer by Layer: Assessing AI Diagnostic Accuracy With Incremental Case Information in Neuroradiology.Cureus · 2025Article
- Preparing for Vascular Surgery Board Certification: A Comparative Study Using Large Language Models.Cureus · 2025Article
- Evolution of patient education materials from large-language artificial intelligence models on complex regional pain syndrome: are patients learning?Proceedings (Baylor University. Medical Center) · 2025Article
- ChatGPT-4 Turbo and Meta's LLaMA 3.1: A Relative Analysis of Answering Radiology Text-Based Questions.Cureus · 2024Article
- Diagnostic Performance of Multimodal Large Language Models for Grading Pediatric Vesicoureteral Reflux on MCUG: A Preliminary Study.Journal of Indian Association of Pediatric SurgeonsArticle
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
AIMS AND
objectivesThis 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.
methodsThe 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.
resultsChatGPT 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.
conclusionChatGPT 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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