TY - JOUR AU - Cheng, Shu-Li AU - Tsai, Shih-Jen AU - Bai, Ya-Mei AU - Ko, Chih-Hung AU - Hsu, Chih-Wei AU - Yang, Fu-Chi AU - Tsai, Chia-Kuang AU - Tu, Yu-Kang AU - Yang, Szu-Nian AU - Tseng, Ping-Tao AU - Hsu, Tien-Wei AU - Liang, Chih-Sung AU - Su, Kuan-Pin PY - 2023 DA - 2023/12/25 TI - Comparisons of Quality, Correctness, and Similarity Between ChatGPT-Generated and Human-Written Abstracts for Basic Research: Cross-Sectional Study JO - J Med Internet Res SP - e51229 VL - 25 KW - ChatGPT KW - abstract KW - AI-generated scientific content KW - plagiarism KW - artificial intelligence KW - NLP KW - natural language processing KW - LLM KW - language model KW - language models KW - text KW - textual KW - generation KW - generative KW - extract KW - extraction KW - scientific research KW - academic research KW - publication KW - publications KW - abstracts AB - Background: ChatGPT may act as a research assistant to help organize the direction of thinking and summarize research findings. However, few studies have examined the quality, similarity (abstracts being similar to the original one), and accuracy of the abstracts generated by ChatGPT when researchers provide full-text basic research papers. Objective: We aimed to assess the applicability of an artificial intelligence (AI) model in generating abstracts for basic preclinical research. Methods: We selected 30 basic research papers from Nature, Genome Biology, and Biological Psychiatry. Excluding abstracts, we inputted the full text into ChatPDF, an application of a language model based on ChatGPT, and we prompted it to generate abstracts with the same style as used in the original papers. A total of 8 experts were invited to evaluate the quality of these abstracts (based on a Likert scale of 0-10) and identify which abstracts were generated by ChatPDF, using a blind approach. These abstracts were also evaluated for their similarity to the original abstracts and the accuracy of the AI content. Results: The quality of ChatGPT-generated abstracts was lower than that of the actual abstracts (10-point Likert scale: mean 4.72, SD 2.09 vs mean 8.09, SD 1.03; P<.001). The difference in quality was significant in the unstructured format (mean difference –4.33; 95% CI –4.79 to –3.86; P<.001) but minimal in the 4-subheading structured format (mean difference –2.33; 95% CI –2.79 to –1.86). Among the 30 ChatGPT-generated abstracts, 3 showed wrong conclusions, and 10 were identified as AI content. The mean percentage of similarity between the original and the generated abstracts was not high (2.10%-4.40%). The blinded reviewers achieved a 93% (224/240) accuracy rate in guessing which abstracts were written using ChatGPT. Conclusions: Using ChatGPT to generate a scientific abstract may not lead to issues of similarity when using real full texts written by humans. However, the quality of the ChatGPT-generated abstracts was suboptimal, and their accuracy was not 100%. SN - 1438-8871 UR - https://www.jmir.org/2023/1/e51229 UR - https://doi.org/10.2196/51229 UR - http://www.ncbi.nlm.nih.gov/pubmed/38145486 DO - 10.2196/51229 ID - info:doi/10.2196/51229 ER -