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用于放射科结构化报告的大型语言模型:GPT-4、ChatGPT-3.5、Perplexity 和 Bing 的性能。

Large language models for structured reporting in radiology: performance of GPT-4, ChatGPT-3.5, Perplexity and Bing.

机构信息

Research Unit of Radiology, Department of Medicine and Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 200, 00128, Rome, RM, Italy.

出版信息

Radiol Med. 2023 Jul;128(7):808-812. doi: 10.1007/s11547-023-01651-4. Epub 2023 May 29.

Abstract

Structured reporting may improve the radiological workflow and communication among physicians. Artificial intelligence applications in medicine are growing fast. Large language models (LLMs) are recently gaining importance as valuable tools in radiology and are currently being tested for the critical task of structured reporting. We compared four LLMs models in terms of knowledge on structured reporting and templates proposal. LLMs hold a great potential for generating structured reports in radiology but additional formal validations are needed on this topic.

摘要

结构化报告可以改善放射科的工作流程和医生之间的沟通。医学人工智能应用发展迅速。大型语言模型(LLM)最近作为放射学中的有价值工具越来越受到重视,并且目前正在针对结构化报告的关键任务进行测试。我们比较了四个 LLM 模型在结构化报告知识和模板建议方面的表现。LLM 在放射科生成结构化报告方面具有巨大潜力,但在这一主题上还需要进行更多的正式验证。

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