及时的工程策略提高了神经放射学病例中 GPT-4 Turbo 的诊断准确性

medRxiv - Radiology and Imaging Pub Date : 2024-05-01 DOI:10.1101/2024.04.29.24306583

Akihiko Wada, Toshiaki Akashi, George Shih, Akifumi Hagiwara, Mitsuo Nishizawa, Yayoi Hayakawa, Junko Kikuta, Keigo Shimoji, Katsuhiro Sano, Koji Kamagata, Atsushi Nakanishi, Shigeki Aoki

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摘要

背景 GPT-4 等大型语言模型（LLMs）在医学图像分析中表现出了良好的能力，但 30-50% 的高误诊率阻碍了它们的实际应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Prompt Engineering Strategies Improve the Diagnostic Accuracy of GPT-4 Turbo in Neuroradiology Cases

Background Large language models (LLMs) like GPT-4 demonstrate promising capabilities in medical image analysis, but their practical utility is hindered by substantial misdiagnosis rates ranging from 30-50%.

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medRxiv - Radiology and Imaging

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