ArticleCureus2024
ChatGPT-4 Turbo and Meta's LLaMA 3.1: A Relative Analysis of Answering 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 1 paper, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- AI screening of nuclear medicine safety breaches: patterns, causes, and opportunities for improved protocols: a systematic review.Annals of nuclear medicine · 2026Pooled it
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
AIMS AND
objectivesThis study aimed to compare the accuracy of two AI models - OpenAI's GPT-4 Turbo (San Francisco, CA) and Meta's LLaMA 3.1 (Menlo Park, CA) - when answering a standardized set of pediatric radiology questions. The primary objective was to evaluate the overall accuracy of each model, while the secondary objective was to assess their performance within subsections. METHODS AND MATERIALS: A total of 79 text-based pediatric radiology questions were selected out of 302 total questions for this comparison. The questions covered seven subsections, including musculoskeletal, chest, and neuroradiology, among others. Image-based questions were excluded to focus on text interpretation and to minimize the sampling bias within each model. Each model was tested independently on the same question set, and the percent accuracy was calculated for both overall performance as well as individual subsections.
resultsGPT-4 Turbo performed at an overall accuracy of 88.6% (70/79 questions), outperforming LLaMA 3.1's 77.2% (61/79). Within subsections, GPT-4 Turbo had higher accuracy in most areas, except for equal accuracy in the neuroradiology section. The subsections with the greatest accuracy for GPT-4 Turbo, in descending order, were chest and cardiac radiology (100%), musculoskeletal system (93.3%), and genitourinary system (92.9%). LLaMA 3.1's highest performance was 86.7% in the musculoskeletal system, while its lowest was 50.0% in chest radiology.
conclusionGPT-4 Turbo consistently outperformed LLaMA 3.1 in answering pediatric radiology questions, both overall and within most subsections. These findings suggest that GPT-4 Turbo may offer more accurate responses in specialized medical education, in contrast to LLaMA 3.1's efficient performance, although future research should further evaluate AI models' performance within other fields.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.