Abstract
This cross-sectional study assessed a generative-AI platform to automate the creation of accurate, appropriate, and compelling social-media (SoMe) posts from urological journal articles. One hundred SoMe-posts from the top 3 journals in urology X (Twitter) profiles were collected from Aug-2022 to Oct-2023 A freeware GPT-tool was developed to auto-generate SoMe posts, which included title-summarization, key findings, pertinent emojis, hashtags, and DOI 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. Median (IQR) time for post auto-generation was 10.2 seconds (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 hashtags 138 (92%). 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). Generative-AI can automate the creation of SoMe posts from urology journal abstracts that are both accurate and preferable by the academic community and general public.
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