GPT-4 能否建议脑磁共振成像的最佳顺序?

Kazufumi Suzuki, Kayoko Abe, Shuji Sakai
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摘要

目的:本研究旨在评估 GPT-4 这一大型语言模型在协助放射科医生确定脑部磁共振成像(MRI)方案方面的潜力:我们使用了一家特定医院的脑磁共振成像协议,涵盖 20 种疾病或检查目的,不包括脑肿瘤协议。GPT-4 在系统提示下为基本脑磁共振成像方案添加一个磁共振成像序列,并输入疾病名称作为用户提示。该模型的建议由两位具有 20 多年相关经验的放射科医生进行评估。根据建议与医院规程的一致性对建议进行如下评分:0分代表不合适,1分代表可接受但不匹配,2分代表与协议相符。实验用日语和英语进行,以比较 GPT-4 在不同语言中的表现:结果:GPT-4 的英语得分为 27/40,日语得分为 28/40。两种语言中,GPT-4 对莫亚莫亚氏病和神经性视脊髓炎提出了不恰当的建议;日语中,GPT-4 对脑梗塞提出了不恰当的建议。对于其他方案,建议的序列要么合适,要么更好。有 7 个方案的英语建议与日语建议不同。结论GPT-4 通常建议合适的 MRI 序列。该研究表明,GPT-4 可以帮助放射科医生确定方案;但是,LLMs 在放射学上的应用还需要进一步研究。
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Can GPT-4 suggest the optimal sequence for brain magnetic resonance imaging?
Purpose: This study aimed to evaluate the potential of GPT-4, a large language model, in assisting radiologists to determine brain magnetic resonance imaging (MRI) protocols. Materials and methods: We used brain MRI protocols from a specific hospital, covering 20 diseases or examination purposes, excluding brain tumor protocols. GPT-4 was given system prompts to add one MRI sequence for the basic brain MRI protocol and disease names were input as user prompts. The model's suggestions were evaluated by two radiologists with over 20 years of relevant experience. Suggestions were scored based on their alignment with the hospital's protocol as follows: 0 for inappropriate, 1 for acceptable but nonmatching, and 2 for matching the protocol. The experiment was conducted in both Japanese and English to compare GPT-4's performance in different languages. Results: GPT-4 scored 27/40 points in English and 28/40 points in Japanese. GPT-4 gave inappropriate suggestions for Moyamoya disease and neuromyelitis optica in both languages and cerebral infarction in Japanese. For the other protocols, the suggested sequences were either appropriate or better. The suggestions in English differed from those in Japanese for seven protocols. Conclusion: GPT-4 generally suggested suitable MRI sequences. The study demonstrates that GPT-4 can help radiologists to determine protocols; however, further research is required for the radiological application of LLMs.
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