Comparisons of Quality, Correctness, and Similarity Between ChatGPT-Generated and Human-Written Abstracts for Basic Research: Cross-Sectional Study
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 fu...
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| Published in | Journal of medical Internet research Vol. 25; no. 1; p. e51229 |
|---|---|
| Main Authors | , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Canada
Journal of Medical Internet Research
25.12.2023
Gunther Eysenbach MD MPH, Associate Professor JMIR Publications |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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%. |
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| AbstractList | BackgroundChatGPT 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. ObjectiveWe aimed to assess the applicability of an artificial intelligence (AI) model in generating abstracts for basic preclinical research. MethodsWe 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. ResultsThe 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. ConclusionsUsing 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%. 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%. 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%. 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%. 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%. |
| Audience | Academic |
| Author | Tseng, Ping-Tao Hsu, Tien-Wei Cheng, Shu-Li Tsai, Shih-Jen Bai, Ya-Mei Tsai, Chia-Kuang Yang, Fu-Chi Su, Kuan-Pin Liang, Chih-Sung Tu, Yu-Kang Yang, Szu-Nian Ko, Chih-Hung Hsu, Chih-Wei |
| AuthorAffiliation | 19 Department of Psychiatry Tri-service Hospital Beitou branch Taipei Taiwan 3 Division of Psychiatry, School of Medicine, National Yang-Ming University Taipei Taiwan 2 Department of Psychiatry Taipei Veterans General Hospital Taipei Taiwan 4 Department of Psychiatry Kaohsiung Medical University Hospital Kaohsiung Taiwan 1 Department of Nursing Mackay Medical College Taipei Taiwan 15 Department of Psychology College of Medical and Health Science Asia University Taichung Taiwan 17 Department of Psychiatry E-Da Dachang Hospital I-Shou University Kaohsiung Taiwan 13 Graduate Institute of Health and Welfare Policy National Yang Ming Chiao Tung University Taipei Taiwan 6 Department of Psychiatry Kaohsiung Municipal Siaogang Hospital Kaohsiung Medical University Kaohsiung Taiwan 16 Prospect Clinic for Otorhinolaryngology and Neurology Kaohsiung Taiwan 9 Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan 12 Department of Psychiatry Armed F |
| AuthorAffiliation_xml | – name: 13 Graduate Institute of Health and Welfare Policy National Yang Ming Chiao Tung University Taipei Taiwan – name: 21 College of Medicine China Medical University Taichung Taiwan – name: 22 Mind-Body Interface Laboratory China Medical University and Hospital Taichung Taiwan – name: 5 Department of Psychiatry College of Medicine Kaohsiung Medical University Kaohsiung Taiwan – name: 17 Department of Psychiatry E-Da Dachang Hospital I-Shou University Kaohsiung Taiwan – name: 14 Institute of Biomedical Sciences Institute of Precision Medicine National Sun Yat-sen University Kaohsiung Taiwan – name: 3 Division of Psychiatry, School of Medicine, National Yang-Ming University Taipei Taiwan – name: 4 Department of Psychiatry Kaohsiung Medical University Hospital Kaohsiung Taiwan – name: 11 Department of Psychiatry Tri-service Hospital, Beitou branch Taipei Taiwan – name: 18 Department of Psychiatry E-Da Hospital I-Shou University Kaohsiung Taiwan – name: 10 Department of Dentistry National Taiwan University Hospital Taipei Taiwan – name: 12 Department of Psychiatry Armed Forces Taoyuan General Hospital Taoyuan Taiwan – name: 2 Department of Psychiatry Taipei Veterans General Hospital Taipei Taiwan – name: 8 Department of Neurology Tri-Service General Hospital National Defense Medical Center Taipei Taiwan – name: 23 An-Nan Hospital China Medical University Tainan Taiwan – name: 6 Department of Psychiatry Kaohsiung Municipal Siaogang Hospital Kaohsiung Medical University Kaohsiung Taiwan – name: 7 Department of Psychiatry Kaohsiung Chang Gung Memorial Hospital Kaohsiung Taiwan – name: 15 Department of Psychology College of Medical and Health Science Asia University Taichung Taiwan – name: 16 Prospect Clinic for Otorhinolaryngology and Neurology Kaohsiung Taiwan – name: 1 Department of Nursing Mackay Medical College Taipei Taiwan – name: 9 Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan – name: 19 Department of Psychiatry Tri-service Hospital Beitou branch Taipei Taiwan – name: 20 Department of Psychiatry National Defense Medical Center Taipei Taiwan |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38145486$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1186/s13054-023-04380-2 10.2139/ssrn.4429014 10.1136/bmj.p1133 10.1016/S2589-7500(23)00023-7 10.2196/46924 10.3390/healthcare11060887 10.1038/s41746-023-00819-6 |
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| Copyright | Shu-Li Cheng, Shih-Jen Tsai, Ya-Mei Bai, Chih-Hung Ko, Chih-Wei Hsu, Fu-Chi Yang, Chia-Kuang Tsai, Yu-Kang Tu, Szu-Nian Yang, Ping-Tao Tseng, Tien-Wei Hsu, Chih-Sung Liang, Kuan-Pin Su. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.12.2023. COPYRIGHT 2023 Journal of Medical Internet Research 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Shu-Li Cheng, Shih-Jen Tsai, Ya-Mei Bai, Chih-Hung Ko, Chih-Wei Hsu, Fu-Chi Yang, Chia-Kuang Tsai, Yu-Kang Tu, Szu-Nian Yang, Ping-Tao Tseng, Tien-Wei Hsu, Chih-Sung Liang, Kuan-Pin Su. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.12.2023. 2023 |
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| Keywords | 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 |
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| License | Shu-Li Cheng, Shih-Jen Tsai, Ya-Mei Bai, Chih-Hung Ko, Chih-Wei Hsu, Fu-Chi Yang, Chia-Kuang Tsai, Yu-Kang Tu, Szu-Nian Yang, Ping-Tao Tseng, Tien-Wei Hsu, Chih-Sung Liang, Kuan-Pin Su. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.12.2023. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
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| PublicationTitle | Journal of medical Internet research |
| PublicationTitleAlternate | J Med Internet Res |
| PublicationYear | 2023 |
| Publisher | Journal of Medical Internet Research Gunther Eysenbach MD MPH, Associate Professor JMIR Publications |
| Publisher_xml | – name: Journal of Medical Internet Research – name: Gunther Eysenbach MD MPH, Associate Professor – name: JMIR Publications |
| References | ref8 ref7 ref9 ref4 ref3 ref6 ref11 ref5 ref10 ref2 ref1 |
| References_xml | – ident: ref1 doi: 10.1186/s13054-023-04380-2 – ident: ref4 doi: 10.2139/ssrn.4429014 – ident: ref5 – ident: ref7 – ident: ref2 doi: 10.1136/bmj.p1133 – ident: ref9 – ident: ref8 – ident: ref10 doi: 10.1016/S2589-7500(23)00023-7 – ident: ref11 doi: 10.2196/46924 – ident: ref6 doi: 10.3390/healthcare11060887 – ident: ref3 doi: 10.1038/s41746-023-00819-6 |
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| Snippet | ChatGPT may act as a research assistant to help organize the direction of thinking and summarize research findings. However, few studies have examined the... Background ChatGPT may act as a research assistant to help organize the direction of thinking and summarize research findings. However, few studies have... Background ChatGPT may act as a research assistant to help organize the direction of thinking and summarize research findings. However, few studies have... Background:ChatGPT may act as a research assistant to help organize the direction of thinking and summarize research findings. However, few studies have... BACKGROUNDChatGPT may act as a research assistant to help organize the direction of thinking and summarize research findings. However, few studies have... BackgroundChatGPT may act as a research assistant to help organize the direction of thinking and summarize research findings. However, few studies have... |
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| SubjectTerms | 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 |
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| Title | Comparisons of Quality, Correctness, and Similarity Between ChatGPT-Generated and Human-Written Abstracts for Basic Research: Cross-Sectional Study |
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