The role of large language models in the writing of surgical reviews: fact or fantasy?

IF 2.2 3区 医学 Q2 SURGERY
Daphne E DeTemple, Simon Störzer, Sahar Arbabzadah, Anna Riddermann, Felix Gronau, Kai Timrott, Hüseyin Bektas, Moritz Kleine
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引用次数: 0

Abstract

Background: Amidst the current enthusiasm concerning artificial intelligence and its possible application in the composition of different kinds of scientific and non-scientific written documents, we evaluated the usage of artificial intelligence for writing surgical short reviews.

Methods: In order to assess the formal and content quality of AI-generated texts compared to human written texts, ten AI-based text generators (five chatbots and five content creators) and four surgeons in training received the same prompt for a short scientific article on a liver surgery theme. All texts were anonymized and subsequently evaluated by three experienced liver surgeons based on a pre-defined scoring scheme, as well as for quality of references and readability according to readability indices. Furthermore, all texts were tested for plagiarism using PlagScan.

Results: Overall percentage of correct assessment for AI/non-AI generation by experienced surgeons lay at 78.57%. Human written text had a mean word count of 1054 versus 874 in AI-generated text, with a higher mean Flesh Reading Ease Score (FRE, 26.2 ± 5.1 versus 17.7 ± 6.1). References were PubMed-listed in 100% for human versus 46% for AI-generated text, with only one non-human text reaching 100% formally correct citation of references. PlagScan found 6.4%±1.3 mean resemblance to existing texts for human versus 7.6%±4.5 for AI-generated text.

Discussion: Overall, AI could already mislead experienced scientific surgeons in 26.7% of cases into believing it to be human. However, formal requirements, especially considering referencing, are still in great need of improvement with only one of AI-generated articles fulfilling our quality requirements.

大型语言模型在外科评论写作中的作用:事实还是幻想?
背景:在当前对人工智能及其在各种科学和非科学书面文件组成中的可能应用的热情中,我们评估了人工智能在撰写外科简短评论中的使用。方法:为了评估人工智能生成文本与人类书面文本相比的形式和内容质量,10个基于人工智能的文本生成器(5个聊天机器人和5个内容创建者)和4个正在培训的外科医生收到了相同的提示,以撰写一篇关于肝脏手术主题的简短科学文章。所有文献均匿名化,随后由三名经验丰富的肝脏外科医生根据预先定义的评分方案进行评估,并根据可读性指数对参考文献的质量和可读性进行评估。此外,使用PlagScan对所有文本进行了抄袭测试。结果:经验丰富的外科医生对人工智能/非人工智能生成的总体正确评估率为78.57%。人类书面文本的平均字数为1054,而人工智能生成文本的平均字数为874,平均阅读轻松得分更高(FRE, 26.2±5.1比17.7±6.1)。人类的参考文献在pubmed上的引用率为100%,而人工智能生成的文本为46%,只有一篇非人类文本的引用达到了100%的正式正确。PlagScan发现人类文本与现有文本的平均相似度为6.4%±1.3,而人工智能生成的文本与现有文本的平均相似度为7.6%±4.5。讨论:总的来说,在26.7%的案例中,人工智能可能已经误导了经验丰富的科学外科医生,让他们相信它是人类。然而,形式上的要求,特别是考虑到引用,仍然需要很大的改进,只有一篇人工智能生成的文章符合我们的质量要求。
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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来源期刊
CiteScore
3.30
自引率
8.70%
发文量
342
审稿时长
4-8 weeks
期刊介绍: Langenbeck''s Archives of Surgery aims to publish the best results in the field of clinical surgery and basic surgical research. The main focus is on providing the highest level of clinical research and clinically relevant basic research. The journal, published exclusively in English, will provide an international discussion forum for the controlled results of clinical surgery. The majority of published contributions will be original articles reporting on clinical data from general and visceral surgery, while endocrine surgery will also be covered. Papers on basic surgical principles from the fields of traumatology, vascular and thoracic surgery are also welcome. Evidence-based medicine is an important criterion for the acceptance of papers.
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