PURPOSE This cross-sectional study assessed a generative artificial intelligence platform to automate the creation of accurate, appropriate, and compelling social media (SoMe) posts from urological journal articles. MATERIALS AND METHODS One hundred SoMe posts from the top 3 journals in urology X (formerly Twitter) profiles were collected from August 2022 to October 2023. A freeware generative pre-trained transformer (GPT) tool was developed to autogenerate SoMe posts, which included title summarization, key findings, pertinent emojis, hashtags, and digital object identifier links to the article. Three physicians independently evaluated GPT-generated posts for achieving tetrafecta of accuracy and appropriateness criteria. Fifteen scenarios were created from 5 randomly selected posts from each journal. Each scenario contained both the original and the GPT-generated post for the same article. Five questions were formulated to investigate the posts' likability, shareability, engagement, understandability, and comprehensiveness. The paired posts were then randomized and presented to blinded academic authors and general public through Amazon Mechanical Turk (AMT) responders for preference evaluation. RESULTS Median time for post autogeneration was 10.2 seconds (interquartile range 8.5-12.5). Of the 150 rated GPT-generated posts, 115 (76.6%) met the correctness tetrafecta: 144 (96%) accurately summarized the title, 147 (98%) accurately presented the articles' main findings, 131 (87.3%) appropriately used emojis, and 138 (92%) appropriately used hashtags. A total of 258 academic urologists and 493 AMT responders answered the surveys, wherein the GPT-generated posts consistently outperformed the original journals' posts for both academicians and AMT responders (P < .05). CONCLUSIONS Generative artificial intelligence can automate the creation of SoMe posts from urology journal abstracts that are both accurate and preferable by the academic community and general public.

中文翻译：

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用于自动化泌尿外科期刊文章社交媒体帖子的生成式人工智能平台：一项横断面研究和随机评估。


目的 这项横断面研究评估了一个生成式人工智能平台，该平台可以从泌尿外科期刊文章中自动创建准确、适当和引人注目的社交媒体 （SoMe） 帖子。材料和方法 从 2022 年 8 月至 2023 年 10 月，收集了来自 urology X（前身为 Twitter）概况前 3 名期刊的 100 篇 SoMe 帖子。开发了一个免费的生成式预训练转换器 （GPT） 工具，用于自动生成 SoMe 帖子，其中包括标题摘要、主要发现、相关表情符号、主题标签和指向文章的数字对象标识符链接。三名医生独立评估了 GPT 生成的帖子达到准确性和适当性标准的四重奏。从每个期刊中随机选择的 5 个帖子中创建了 15 个场景。每个场景都包含同一篇文章的原始帖子和 GPT 生成的帖子。制定了五个问题来调查帖子的受欢迎程度、可分享性、参与度、可理解性和全面性。然后将配对的帖子随机化，并通过 Amazon Mechanical Turk （AMT） 响应者呈现给不知情的学术作者和公众，以进行偏好评估。结果 后自动生成的中位时间为 10.2 秒（四分位距 8.5-12.5）。在 150 篇评分的 GPT 生成的帖子中，115 篇 （76.6%） 符合正确性四重奏：144 篇 （96%） 准确总结了标题，147 篇 （98%） 准确呈现了文章的主要发现，131 篇 （87.3%） 适当使用了表情符号，138 篇 （92%） 适当使用了主题标签。共有 258 名学术泌尿科医生和 493 名 AMT 回复者回答了调查，其中 GPT 生成的帖子在院士和 AMT 回复者中的表现始终优于原始期刊的帖子 （P < .05）。 结论 生成式人工智能可以自动从泌尿外科期刊摘要中创建 SoMe 帖子，这些帖子既准确又受到学术界和公众的青睐。