Comparison of the accuracy performances of the Gemini Advanced, the GPT-4, the Copilot, and the GPT-3.5 models in medical imaging systems: A Zero-shot prompting analysis

Abstract

Large Language Models (LLMs) have gained popularity across healthcare and attracted the attention of researchers of various medical specialties. Determining which model performs well in which circumstances is essential for accurate results. This study aims to compare the accuracy of recently developed LLMs for medical imaging systems and to evaluate the reliability of LLMs in terms of correct responses. A total of 400 questions were divided into four categories: X-ray, ultrasound, magnetic resonance imaging, and nuclear medicine. LLMs’ responses were evaluated with a zero-prompting approach by measuring the percentage of correct answers. McNemar tests were used to evaluate the significance of differences between models, and Cohen kappa statistics were used to determine the reliability of the models. Gemini Advanced, GPT-4, Copilot, and GPT-3.5 resulted in accuracy rates of 86.25%, 84.25%, 77.5%, and 59.75%, respectively. There was a strong correlation between Gemini Advanced and the GPT-4 compared with other models, К=0.762. This study is the first that analyzes the accuracy of responses of recently developed LLMs: Gemini Advanced, GPT-4, Copilot, and GPT-3.5 on questions related to medical imaging systems. And a comprehensive dataset with three question types was created within medical imaging systems, which was evenly distributed from various sources.

Ähnliche Arbeiten