Comparisons of Quality, Correctness, and Similarity Between ChatGPT-Generated and Human-Written Abstracts for Basic Research: Cross-Sectional Study

• Published in: Journal of medical Internet research Vol. 25; no. 1; p. e51229
• Main Authors: Cheng, Shu-Li, Tsai, Shih-Jen, Bai, Ya-Mei, Ko, Chih-Hung, Hsu, Chih-Wei, Yang, Fu-Chi, Tsai, Chia-Kuang, Tu, Yu-Kang, Yang, Szu-Nian, Tseng, Ping-Tao, Hsu, Tien-Wei, Liang, Chih-Sung, Su, Kuan-Pin
• Format: Journal Article
• Language: English
• Published: Canada Journal of Medical Internet Research 25.12.2023; Gunther Eysenbach MD MPH, Associate Professor; JMIR Publications
• Subjects: Accuracy; Artificial Intelligence; Biology; Chatbots; Computational linguistics; Cross-Sectional Studies; Genomes; Genomics; Humans; Language; Language processing; Likert scale; Multimedia; Natural language interfaces; Original Paper; Plagiarism; Psychiatry; Research Personnel; Variables; generation; scientific research; natural language processing; abstracts; plagiarism; language model; AI-generated scientific content; abstract; LLM; academic research; extraction; artificial intelligence; textual; generative; NLP; extract; ChatGPT; publication; text; language models; publications
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/51229

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. We aimed to assess the applicability of an artificial intelligence (AI) model in generating abstracts for basic preclinical research. 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. 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. 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%.