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Muthanna A, Chaaban Y, Qadhi S. A model of the interrelationship between research ethics and research integrity. Int J Qual Stud Health Well-being 2024; 19:2295151. [PMID: 38126140 PMCID: PMC10763899 DOI: 10.1080/17482631.2023.2295151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
Purpose: The purpose of this article is to explore the interrelationship between research ethics and research integrity with a focus on the primary forms of research misconduct, including plagiarism, fabrication, and falsification. It also details the main factors for their occurrence, and the possible ways for mitigating their use among scholars.Methods: The method employed a detailed examination of the main ethical dilemmas, as delineated in literature, as well as the factors leading to these ethical breaches and the strategies to mitigate them. Further, the teaching experiences of the primary author are reflected in the development of the model.Results: The results of this article are represented in a model illustrating the interrelationship between research ethics and research integrity. Further, a significant aspect of our article is the identification of novel forms of research misconduct concerning the use of irrelevant or forced citations or references.Conclusion: In conclusion, the article highlights the substantial positive effects that adherence to research ethics and integrity have on the academic well-being of scholars.
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Affiliation(s)
- Abdulghani Muthanna
- Department of Teacher Education, Norwegian University of Science and Technology, Trondheim, Norway
| | - Youmen Chaaban
- Educational Research Center, Qatar University, Doha, Qatar
| | - Saba Qadhi
- Core Curriculum Program, Qatar University, Doha, Qatar
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2
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Nazarovets S, Teixeira da Silva JA. ChatGPT as an "author": Bibliometric analysis to assess the validity of authorship. Account Res 2024:1-11. [PMID: 38693669 DOI: 10.1080/08989621.2024.2345713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 04/08/2024] [Indexed: 05/03/2024]
Abstract
Background: Following the 2023 surge in popularity of large language models like ChatGPT, significant ethical discussions emerged regarding their role in academic authorship. Notable ethics organizations, including the ICMJE and COPE, alongside leading publishers, have instituted ethics clauses explicitly stating that such models do not meet the criteria for authorship due to accountability issues.Objective: This study aims to assess the prevalence and ethical implications of listing ChatGPT as an author on academic papers, in violation of existing ethical guidelines set by the ICMJE and COPE.Methods: We conducted a comprehensive review using databases such as Web of Science and Scopus to identify instances where ChatGPT was credited as an author, co-author, or group author.Results: Our search identified 14 papers featuring ChatGPT in such roles. In four of those papers, ChatGPT was listed as an "author" alongside the journal's editor or editor-in-chief. Several of the ChatGPT-authored papers have accrued dozens, even hundreds of citations according to Scopus, Web of Science, and Google Scholar.Discussion: The inclusion of ChatGPT as an author on these papers raises critical questions about the definition of authorship and the accountability mechanisms in place for content produced by artificial intelligence. Despite the ethical guidelines, the widespread citation of these papers suggests a disconnect between ethical policy and academic practice.Conclusion: The findings suggest a need for corrective measures to address these discrepancies. Immediate review and amendment of the listed papers is advised, highlighting a significant oversight in the enforcement of ethical standards in academic publishing.
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Affiliation(s)
- Serhii Nazarovets
- Library, Borys Grinchenko Kyiv Metropolitan University, Kyiv, Ukraine
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3
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Glynn A. The case for universal artificial intelligence declaration on the precedent of conflict of interest. Account Res 2024:1-2. [PMID: 38643483 DOI: 10.1080/08989621.2024.2345719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Affiliation(s)
- Alex Glynn
- Kornhauser Health Sciences Library, University of Louisville, Louisville, KY, USA
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4
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Hughes RC, van Heerden A. PLOS-LLM: Can and should AI enable a new paradigm of scientific knowledge sharing? PLOS DIGITAL HEALTH 2024; 3:e0000501. [PMID: 38662633 PMCID: PMC11045089 DOI: 10.1371/journal.pdig.0000501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Affiliation(s)
- Robert C. Hughes
- Maternal and Child Health Intervention Research Group, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Alastair van Heerden
- Centre for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa
- SAMRC/Wits Developmental Pathways for Health Research Unit, University of the Witwatersrand, Johannesburg, South Africa
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5
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Kwon DY, Villavisanis DF, Oleru O, Seyidova N, Kiani SN, Russell J, Taub PJ. Implications for the Use of Artificial Intelligence in Plastic Surgery Research and Practice. Plast Reconstr Surg 2024; 153:862e-863e. [PMID: 37699114 PMCID: PMC10961169 DOI: 10.1097/prs.0000000000011057] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Affiliation(s)
- Daniel Y. Kwon
- Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Dillan F. Villavisanis
- Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Olachi Oleru
- Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Nargiz Seyidova
- Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Sara N. Kiani
- Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Jeffrey Russell
- Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Peter J. Taub
- Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
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Lin KC, Chen TA, Lin MH, Chen YC, Chen TJ. Integration and Assessment of ChatGPT in Medical Case Reporting: A Multifaceted Approach. Eur J Investig Health Psychol Educ 2024; 14:888-901. [PMID: 38667812 PMCID: PMC11049282 DOI: 10.3390/ejihpe14040057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 04/28/2024] Open
Abstract
ChatGPT, a large language model, has gained significance in medical writing, particularly in case reports that document the course of an illness. This article explores the integration of ChatGPT and how ChatGPT shapes the process, product, and politics of medical writing in the real world. We conducted a bibliometric analysis on case reports utilizing ChatGPT and indexed in PubMed, encompassing publication information. Furthermore, an in-depth analysis was conducted to categorize the applications and limitations of ChatGPT and the publication trend of application categories. A total of 66 case reports utilizing ChatGPT were identified, with a predominant preference for the online version and English input by the authors. The prevalent application categories were information retrieval and content generation. Notably, this trend remained consistent across different months. Within the subset of 32 articles addressing ChatGPT limitations in case report writing, concerns related to inaccuracies and a lack of clinical context were prominently emphasized. This pointed out the important role of clinical thinking and professional expertise, representing the foundational tenets of medical education, while also accentuating the distinction between physicians and generative artificial intelligence.
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Affiliation(s)
- Kuan-Chen Lin
- Department of Family Medicine, Taipei Veterans General Hospital, No. 201, Sec. 2, Shih-Pai Road, Taipei 11217, Taiwan; (K.-C.L.); (T.-A.C.); (M.-H.L.)
| | - Tsung-An Chen
- Department of Family Medicine, Taipei Veterans General Hospital, No. 201, Sec. 2, Shih-Pai Road, Taipei 11217, Taiwan; (K.-C.L.); (T.-A.C.); (M.-H.L.)
| | - Ming-Hwai Lin
- Department of Family Medicine, Taipei Veterans General Hospital, No. 201, Sec. 2, Shih-Pai Road, Taipei 11217, Taiwan; (K.-C.L.); (T.-A.C.); (M.-H.L.)
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 30010, Taiwan
| | - Yu-Chun Chen
- Department of Family Medicine, Taipei Veterans General Hospital, No. 201, Sec. 2, Shih-Pai Road, Taipei 11217, Taiwan; (K.-C.L.); (T.-A.C.); (M.-H.L.)
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 30010, Taiwan
- Institute of Hospital and Health Care Administration, School of Medicine, National Yang Ming Chiao Tung University, Taipei 30010, Taiwan
- Big Data Center, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Tzeng-Ji Chen
- Department of Family Medicine, Taipei Veterans General Hospital Hsinchu Branch, No. 81, Sec. 1, Zhongfeng Road, Zhudong Township, Hsinchu 310403, Taiwan
- Department of Post-Baccalaureate Medicine, National Chung Hsing University, No. 145, Xingda Road, South District, Taichung 402202, Taiwan
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Benaim EH, Wase S, Zaidi S, Monk A, Klatt-Cromwell C, Thorp BD, Ebert CS, Kimple AJ, Senior BA. Detection of plagiarism among rhinology scientific journals. Int Forum Allergy Rhinol 2024. [PMID: 38526947 DOI: 10.1002/alr.23347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/06/2024] [Accepted: 03/10/2024] [Indexed: 03/27/2024]
Abstract
KEY POINTS Automated plagiarism-checking software can be a valuable tool for detecting plagiarism in manuscripts. Twenty-five of 60 articles (42%) had at least one incidence of plagiarism, predominately text recycling. A "similarity score" ranging from 22% to 35% could be a potential cut-off value when screening submitted manuscripts.
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Affiliation(s)
- Ezer H Benaim
- Department of Otolaryngology-Head & Neck Surgery, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Saima Wase
- Department of Otolaryngology-Head & Neck Surgery, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Saif Zaidi
- School of Medicine, University of Paris Cité, Paris, France
| | - Aurelia Monk
- Department of Otolaryngology-Head & Neck Surgery, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Cristine Klatt-Cromwell
- Department of Otolaryngology-Head & Neck Surgery, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Brian D Thorp
- Department of Otolaryngology-Head & Neck Surgery, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Charles S Ebert
- Department of Otolaryngology-Head & Neck Surgery, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Adam J Kimple
- Department of Otolaryngology-Head & Neck Surgery, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Brent A Senior
- Department of Otolaryngology-Head & Neck Surgery, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA
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Kaebnick GE, Magnus DC, Kao A, Hosseini M, Resnik D, Dubljević V, Rentmeester C, Gordijn B, Cherry MJ. Editors' Statement on the Responsible Use of Generative AI Technologies in Scholarly Journal Publishing. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2024; 24:5-8. [PMID: 38085888 PMCID: PMC11218843 DOI: 10.1080/15265161.2023.2292437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
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Zhang H, Jethani N, Jones S, Genes N, Major VJ, Jaffe IS, Cardillo AB, Heilenbach N, Ali NF, Bonanni LJ, Clayburn AJ, Khera Z, Sadler EC, Prasad J, Schlacter J, Liu K, Silva B, Montgomery S, Kim EJ, Lester J, Hill TM, Avoricani A, Chervonski E, Davydov J, Small W, Chakravartty E, Grover H, Dodson JA, Brody AA, Aphinyanaphongs Y, Masurkar A, Razavian N. Evaluating Large Language Models in Extracting Cognitive Exam Dates and Scores. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.07.10.23292373. [PMID: 38405784 PMCID: PMC10888985 DOI: 10.1101/2023.07.10.23292373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Importance Large language models (LLMs) are crucial for medical tasks. Ensuring their reliability is vital to avoid false results. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR. Objective Evaluate ChatGPT and LlaMA-2 performance in extracting MMSE and CDR scores, including their associated dates. Methods Our data consisted of 135,307 clinical notes (Jan 12th, 2010 to May 24th, 2023) mentioning MMSE, CDR, or MoCA. After applying inclusion criteria 34,465 notes remained, of which 765 underwent ChatGPT (GPT-4) and LlaMA-2, and 22 experts reviewed the responses. ChatGPT successfully extracted MMSE and CDR instances with dates from 742 notes. We used 20 notes for fine-tuning and training the reviewers. The remaining 722 were assigned to reviewers, with 309 each assigned to two reviewers simultaneously. Inter-rater-agreement (Fleiss' Kappa), precision, recall, true/false negative rates, and accuracy were calculated. Our study follows TRIPOD reporting guidelines for model validation. Results For MMSE information extraction, ChatGPT (vs. LlaMA-2) achieved accuracy of 83% (vs. 66.4%), sensitivity of 89.7% (vs. 69.9%), true-negative rates of 96% (vs 60.0%), and precision of 82.7% (vs 62.2%). For CDR the results were lower overall, with accuracy of 87.1% (vs. 74.5%), sensitivity of 84.3% (vs. 39.7%), true-negative rates of 99.8% (98.4%), and precision of 48.3% (vs. 16.1%). We qualitatively evaluated the MMSE errors of ChatGPT and LlaMA-2 on double-reviewed notes. LlaMA-2 errors included 27 cases of total hallucination, 19 cases of reporting other scores instead of MMSE, 25 missed scores, and 23 cases of reporting only the wrong date. In comparison, ChatGPT's errors included only 3 cases of total hallucination, 17 cases of wrong test reported instead of MMSE, and 19 cases of reporting a wrong date. Conclusions In this diagnostic/prognostic study of ChatGPT and LlaMA-2 for extracting cognitive exam dates and scores from clinical notes, ChatGPT exhibited high accuracy, with better performance compared to LlaMA-2. The use of LLMs could benefit dementia research and clinical care, by identifying eligible patients for treatments initialization or clinical trial enrollments. Rigorous evaluation of LLMs is crucial to understanding their capabilities and limitations.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Abraham A Brody
- NYU Rory Meyers College of Nursing, NYU Grossman School of Medicine
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Miao J, Thongprayoon C, Suppadungsuk S, Garcia Valencia OA, Qureshi F, Cheungpasitporn W. Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review. Clin Pract 2023; 14:89-105. [PMID: 38248432 PMCID: PMC10801601 DOI: 10.3390/clinpract14010008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/23/2023] [Accepted: 12/28/2023] [Indexed: 01/23/2024] Open
Abstract
The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI's capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an "AI scrutiny" phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI's role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI's involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI's capabilities while upholding stringent academic standards can be envisioned.
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Affiliation(s)
- Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.); (O.A.G.V.); (F.Q.); (W.C.)
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.); (O.A.G.V.); (F.Q.); (W.C.)
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.); (O.A.G.V.); (F.Q.); (W.C.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bang Phli 10540, Samut Prakan, Thailand
| | - Oscar A. Garcia Valencia
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.); (O.A.G.V.); (F.Q.); (W.C.)
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.); (O.A.G.V.); (F.Q.); (W.C.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.); (O.A.G.V.); (F.Q.); (W.C.)
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Kaebnick GE, Magnus DC, Kao A, Hosseini M, Resnik D, Dubljević V, Rentmeester C, Gordijn B, Cherry MJ. Editors' statement on the responsible use of generative AI technologies in scholarly journal publishing. MEDICINE, HEALTH CARE, AND PHILOSOPHY 2023; 26:499-503. [PMID: 37863860 PMCID: PMC10725843 DOI: 10.1007/s11019-023-10176-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2023]
Abstract
Generative artificial intelligence (AI) has the potential to transform many aspects of scholarly publishing. Authors, peer reviewers, and editors might use AI in a variety of ways, and those uses might augment their existing work or might instead be intended to replace it. We are editors of bioethics and humanities journals who have been contemplating the implications of this ongoing transformation. We believe that generative AI may pose a threat to the goals that animate our work but could also be valuable for achieving those goals. In the interests of fostering a wider conversation about how generative AI may be used, we have developed a preliminary set of recommendations for its use in scholarly publishing. We hope that the recommendations and rationales set out here will help the scholarly community navigate toward a deeper understanding of the strengths, limits, and challenges of AI for responsible scholarly work.
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Affiliation(s)
| | | | - Audiey Kao
- American Medical Association, Chicago, USA
| | | | - David Resnik
- U.S. National Institute of Environmental Health Sciences, Durham, USA
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Hryciw BN, Seely AJE, Kyeremanteng K. Guiding principles and proposed classification system for the responsible adoption of artificial intelligence in scientific writing in medicine. Front Artif Intell 2023; 6:1283353. [PMID: 38035200 PMCID: PMC10687472 DOI: 10.3389/frai.2023.1283353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023] Open
Abstract
The integration of large language models (LLMs) and artificial intelligence (AI) into scientific writing, especially in medical literature, presents both unprecedented opportunities and inherent challenges. This manuscript evaluates the transformative potential of LLMs for the synthesis of information, linguistic enhancements, and global knowledge dissemination. At the same time, it raises concerns about unintentional plagiarism, the risk of misinformation, data biases, and an over-reliance on AI. To address these, we propose governing principles for AI adoption that ensure integrity, transparency, validity, and accountability. Additionally, guidelines for reporting AI involvement in manuscript development are delineated, and a classification system to specify the level of AI assistance is introduced. This approach uniquely addresses the challenges of AI in scientific writing, emphasizing transparency in authorship, qualification of AI involvement, and ethical considerations. Concerns regarding access equity, potential biases in AI-generated content, authorship dynamics, and accountability are also explored, emphasizing the human author's continued responsibility. Recommendations are made for fostering collaboration between AI developers, researchers, and journal editors and for emphasizing the importance of AI's responsible use in academic writing. Regular evaluations of AI's impact on the quality and biases of medical manuscripts are also advocated. As we navigate the expanding realm of AI in scientific discourse, it is crucial to maintain the human element of creativity, ethics, and oversight, ensuring that the integrity of scientific literature remains uncompromised.
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Affiliation(s)
- Brett N. Hryciw
- Division of Critical Care, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Andrew J. E. Seely
- Division of Thoracic Surgery, Department of Surgery, The Ottawa Hospital, Ottawa, ON, Canada
- Clinical Epidemiology, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Kwadwo Kyeremanteng
- Division of Critical Care, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
- Clinical Epidemiology, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
- Institute du Savoir Montfort, Ottawa, ON, Canada
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Li D, Zhang Z. MetaQA: Enhancing human-centered data search using Generative Pre-trained Transformer (GPT) language model and artificial intelligence. PLoS One 2023; 18:e0293034. [PMID: 37956160 PMCID: PMC10642800 DOI: 10.1371/journal.pone.0293034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 10/03/2023] [Indexed: 11/15/2023] Open
Abstract
Accessing and utilizing geospatial data from various sources is essential for developing scientific research to address complex scientific and societal challenges that require interdisciplinary knowledge. The traditional keyword-based geosearch approach is insufficient due to the uncertainty inherent within spatial information and how it is presented in the data-sharing platform. For instance, the Gulf of Mexico Coastal Ocean Observing System (GCOOS) data search platform stores geoinformation and metadata in a complex tabular. Users can search for data by entering keywords or selecting data from a drop-down manual from the user interface. However, the search results provide limited information about the data product, where detailed descriptions, potential use, and relationship with other data products are still missing. Language models (LMs) have demonstrated great potential in tasks like question answering, sentiment analysis, text classification, and machine translation. However, they struggle when dealing with metadata represented in tabular format. To overcome these challenges, we developed Meta Question Answering System (MetaQA), a novel spatial data search model. MetaQA integrates end-to-end AI models with a generative pre-trained transformer (GPT) to enhance geosearch services. Using GCOOS metadata as a case study, we tested the effectiveness of MetaQA. The results revealed that MetaQA outperforms state-of-the-art question-answering models in handling tabular metadata, underlining its potential for user-inspired geosearch services.
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Affiliation(s)
- Diya Li
- Department of Geography, Texas A&M University, College Station, Texas, United States of America
| | - Zhe Zhang
- Department of Geography, Texas A&M University, College Station, Texas, United States of America
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, United States of America
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Rahimi F, Talebi Bezmin Abadi A. Passive Contribution of ChatGPT to Scientific Papers. Ann Biomed Eng 2023; 51:2340-2350. [PMID: 37284995 DOI: 10.1007/s10439-023-03260-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 05/26/2023] [Indexed: 06/08/2023]
Abstract
Arguably ChatGPT jeopardizes the integrity and validity of the academic publications instead of ethically facilitating them. ChatGPT can apparently fulfill a portion of one of the four authorship criteria set by the International Committee of Medical Journal Editors (ICMJE), i.e., "drafting." However, the authorship criteria by ICMJE must all be collectively met, not singly or partially. Many published manuscripts or preprints have credited ChatGPT by including it in the author byline, and the academic publishing enterprise seems to be unguided on how to handle such manuscripts. Interestingly, PLoS Digital Health removed ChatGPT off a paper which had ChatGPT listed initially in the author byline of the preprint version. Revised publishing policies are, thus, promptly required to guide a consistent stance regarding ChatGPT or similar artificial content generators. Publishing policies must accord among publishers, preprint servers ( https://asapbio.org/preprint-servers ), universities, and research institutions worldwide and across different disciplines. Ideally, considering any declaration of the contribution of ChatGPT to writing any scientific article should be recognized as publishing misconduct immediately and be retracted. Meanwhile, all parties involved in the scientific reporting and publishing must be educated about how ChatGPT fails to meet the essential authorship criteria, so that no author must submit a manuscript with ChatGPT contributing as a "co-author." Meanwhile, using ChatGPT for writing laboratory reports or short summaries of experiments may be acceptable, but not for academic publishing or formal scientific reporting.
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Affiliation(s)
- Farid Rahimi
- Research School of Biology, The Australian National University, Ngunnawal and Ngambri Country, Canberra, ACT, Australia.
| | - Amin Talebi Bezmin Abadi
- Department of Bacteriology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
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Pantanowitz J, Pantanowitz L. Implications of ChatGPT for cytopathology and recommendations for updating JASC guidelines on the responsible use of artificial intelligence. J Am Soc Cytopathol 2023; 12:389-394. [PMID: 37714732 DOI: 10.1016/j.jasc.2023.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/01/2023] [Accepted: 07/03/2023] [Indexed: 09/17/2023]
Affiliation(s)
- Joshua Pantanowitz
- Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
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Hosseini M, Gao CA, Liebovitz DM, Carvalho AM, Ahmad FS, Luo Y, MacDonald N, Holmes KL, Kho A. An exploratory survey about using ChatGPT in education, healthcare, and research. PLoS One 2023; 18:e0292216. [PMID: 37796786 PMCID: PMC10553335 DOI: 10.1371/journal.pone.0292216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/14/2023] [Indexed: 10/07/2023] Open
Abstract
OBJECTIVE ChatGPT is the first large language model (LLM) to reach a large, mainstream audience. Its rapid adoption and exploration by the population at large has sparked a wide range of discussions regarding its acceptable and optimal integration in different areas. In a hybrid (virtual and in-person) panel discussion event, we examined various perspectives regarding the use of ChatGPT in education, research, and healthcare. MATERIALS AND METHODS We surveyed in-person and online attendees using an audience interaction platform (Slido). We quantitatively analyzed received responses on questions about the use of ChatGPT in various contexts. We compared pairwise categorical groups with a Fisher's Exact. Furthermore, we used qualitative methods to analyze and code discussions. RESULTS We received 420 responses from an estimated 844 participants (response rate 49.7%). Only 40% of the audience had tried ChatGPT. More trainees had tried ChatGPT compared with faculty. Those who had used ChatGPT were more interested in using it in a wider range of contexts going forwards. Of the three discussed contexts, the greatest uncertainty was shown about using ChatGPT in education. Pros and cons were raised during discussion for the use of this technology in education, research, and healthcare. DISCUSSION There was a range of perspectives around the uses of ChatGPT in education, research, and healthcare, with still much uncertainty around its acceptability and optimal uses. There were different perspectives from respondents of different roles (trainee vs faculty vs staff). More discussion is needed to explore perceptions around the use of LLMs such as ChatGPT in vital sectors such as education, healthcare and research. Given involved risks and unforeseen challenges, taking a thoughtful and measured approach in adoption would reduce the likelihood of harm.
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Affiliation(s)
- Mohammad Hosseini
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Catherine A. Gao
- Division of Pulmonary and Critical Care, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - David M. Liebovitz
- Divisions of General Internal Medicine and Health and Biomedical Informatics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Center for Medical Education in Digital Health and Data Science, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Alexandre M. Carvalho
- Division of Infectious Diseases, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Center for Pathogen Genomics & Microbial Evolution, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Faraz S. Ahmad
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Bluhm Cardiovascular Center for Artificial Intelligence, Northwestern Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Ngan MacDonald
- Institute for Artificial Intelligence in Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Kristi L. Holmes
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Institute for Artificial Intelligence in Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Galter Health Sciences Library and Learning Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Abel Kho
- Divisions of General Internal Medicine and Health and Biomedical Informatics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Institute for Artificial Intelligence in Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
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Goodman RS, Patrinely JR, Stone CA, Zimmerman E, Donald RR, Chang SS, Berkowitz ST, Finn AP, Jahangir E, Scoville EA, Reese TS, Friedman DL, Bastarache JA, van der Heijden YF, Wright JJ, Ye F, Carter N, Alexander MR, Choe JH, Chastain CA, Zic JA, Horst SN, Turker I, Agarwal R, Osmundson E, Idrees K, Kiernan CM, Padmanabhan C, Bailey CE, Schlegel CE, Chambless LB, Gibson MK, Osterman TJ, Wheless LE, Johnson DB. Accuracy and Reliability of Chatbot Responses to Physician Questions. JAMA Netw Open 2023; 6:e2336483. [PMID: 37782499 PMCID: PMC10546234 DOI: 10.1001/jamanetworkopen.2023.36483] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 08/22/2023] [Indexed: 10/03/2023] Open
Abstract
Importance Natural language processing tools, such as ChatGPT (generative pretrained transformer, hereafter referred to as chatbot), have the potential to radically enhance the accessibility of medical information for health professionals and patients. Assessing the safety and efficacy of these tools in answering physician-generated questions is critical to determining their suitability in clinical settings, facilitating complex decision-making, and optimizing health care efficiency. Objective To assess the accuracy and comprehensiveness of chatbot-generated responses to physician-developed medical queries, highlighting the reliability and limitations of artificial intelligence-generated medical information. Design, Setting, and Participants Thirty-three physicians across 17 specialties generated 284 medical questions that they subjectively classified as easy, medium, or hard with either binary (yes or no) or descriptive answers. The physicians then graded the chatbot-generated answers to these questions for accuracy (6-point Likert scale with 1 being completely incorrect and 6 being completely correct) and completeness (3-point Likert scale, with 1 being incomplete and 3 being complete plus additional context). Scores were summarized with descriptive statistics and compared using the Mann-Whitney U test or the Kruskal-Wallis test. The study (including data analysis) was conducted from January to May 2023. Main Outcomes and Measures Accuracy, completeness, and consistency over time and between 2 different versions (GPT-3.5 and GPT-4) of chatbot-generated medical responses. Results Across all questions (n = 284) generated by 33 physicians (31 faculty members and 2 recent graduates from residency or fellowship programs) across 17 specialties, the median accuracy score was 5.5 (IQR, 4.0-6.0) (between almost completely and complete correct) with a mean (SD) score of 4.8 (1.6) (between mostly and almost completely correct). The median completeness score was 3.0 (IQR, 2.0-3.0) (complete and comprehensive) with a mean (SD) score of 2.5 (0.7). For questions rated easy, medium, and hard, the median accuracy scores were 6.0 (IQR, 5.0-6.0), 5.5 (IQR, 5.0-6.0), and 5.0 (IQR, 4.0-6.0), respectively (mean [SD] scores were 5.0 [1.5], 4.7 [1.7], and 4.6 [1.6], respectively; P = .05). Accuracy scores for binary and descriptive questions were similar (median score, 6.0 [IQR, 4.0-6.0] vs 5.0 [IQR, 3.4-6.0]; mean [SD] score, 4.9 [1.6] vs 4.7 [1.6]; P = .07). Of 36 questions with scores of 1.0 to 2.0, 34 were requeried or regraded 8 to 17 days later with substantial improvement (median score 2.0 [IQR, 1.0-3.0] vs 4.0 [IQR, 2.0-5.3]; P < .01). A subset of questions, regardless of initial scores (version 3.5), were regenerated and rescored using version 4 with improvement (mean accuracy [SD] score, 5.2 [1.5] vs 5.7 [0.8]; median score, 6.0 [IQR, 5.0-6.0] for original and 6.0 [IQR, 6.0-6.0] for rescored; P = .002). Conclusions and Relevance In this cross-sectional study, chatbot generated largely accurate information to diverse medical queries as judged by academic physician specialists with improvement over time, although it had important limitations. Further research and model development are needed to correct inaccuracies and for validation.
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Affiliation(s)
| | - J. Randall Patrinely
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Cosby A. Stone
- Department of Allergy, Pulmonology, and Critical Care, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Eli Zimmerman
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Rebecca R. Donald
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Sam S. Chang
- Department of Urology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Sean T. Berkowitz
- Vanderbilt Eye Institute, Department of Ophthalmology, Vanderbilt University Medical, Nashville, Tennessee
| | - Avni P. Finn
- Vanderbilt Eye Institute, Department of Ophthalmology, Vanderbilt University Medical, Nashville, Tennessee
| | - Eiman Jahangir
- Department of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Elizabeth A. Scoville
- Department of Gastroenterology, Hepatology, and Nutrition, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Tyler S. Reese
- Department of Rheumatology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Debra L. Friedman
- Department of Pediatric Hematology/Oncology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Julie A. Bastarache
- Department of Allergy, Pulmonology, and Critical Care, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Yuri F. van der Heijden
- Department of Infectious Disease, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jordan J. Wright
- Department of Diabetes, Endocrinology, and Metabolism, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Fei Ye
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Nicholas Carter
- Division of Trauma and Surgical Critical Care, University of Miami Miller School of Medicine, Miami, Florida
| | - Matthew R. Alexander
- Department of Cardiovascular Medicine and Clinical Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jennifer H. Choe
- Department of Hematology/Oncology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Cody A. Chastain
- Department of Infectious Disease, Vanderbilt University Medical Center, Nashville, Tennessee
| | - John A. Zic
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Sara N. Horst
- Department of Gastroenterology, Hepatology, and Nutrition, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Isik Turker
- Department of Cardiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Rajiv Agarwal
- Department of Hematology/Oncology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Evan Osmundson
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Kamran Idrees
- Department of Surgical Oncology & Endocrine Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Colleen M. Kiernan
- Department of Surgical Oncology & Endocrine Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Chandrasekhar Padmanabhan
- Department of Surgical Oncology & Endocrine Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Christina E. Bailey
- Department of Surgical Oncology & Endocrine Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Cameron E. Schlegel
- Department of Surgical Oncology & Endocrine Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Lola B. Chambless
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Michael K. Gibson
- Department of Hematology/Oncology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Travis J. Osterman
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Lee E. Wheless
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Douglas B. Johnson
- Department of Hematology/Oncology, Vanderbilt University Medical Center, Nashville, Tennessee
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Boruff JT, Kraft M, Carroll AJ. Introducing the Journal of the Medical Library Association's policy on the use of generative artificial intelligence in submissions. J Med Libr Assoc 2023; 111:747-749. [PMID: 37928115 PMCID: PMC10621693 DOI: 10.5195/jmla.2023.1826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2023] Open
Abstract
With the arrival of ChatGPT, the academic community has expressed concerns about how generative artificial intelligence will be used by students and researchers alike. After consulting policies from other journals and discussing among the editorial team, we have created a policy on the use of AI on submissions to JMLA. This editorial provides a brief background on these concerns and introduces our policy.
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Affiliation(s)
- Jill T Boruff
- , Co-Lead Editor, Journal of the Medical Library Association, Associate Librarian, Schulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Montreal, QC, Canada
| | - Michelle Kraft
- , Co-Lead Editor, Journal of the Medical Library Association, Medical Library Director, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Alexander J Carroll
- , Associate Director, Stevenson Science and Engineering Library, Vanderbilt University, Nashville, TN, United States
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19
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Kaebnick GE, Magnus DC, Kao A, Hosseini M, Resnik D, Dubljević V, Rentmeester C, Gordijn B, Cherry MJ. Editors' Statement on the Responsible Use of Generative AI Technologies in Scholarly Journal Publishing. AJOB Neurosci 2023; 14:337-340. [PMID: 37856337 PMCID: PMC11027931 DOI: 10.1080/21507740.2023.2257181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Generative artificial intelligence (AI) has the potential to transform many aspects of scholarly publishing. Authors, peer reviewers, and editors might use AI in a variety of ways, and those uses might augment their existing work or might instead be intended to replace it. We are editors of bioethics and humanities journals who have been contemplating the implications of this ongoing transformation. We believe that generative AI may pose a threat to the goals that animate our work but could also be valuable for achieving those goals. In the interests of fostering a wider conversation about how generative AI may be used, we have developed a preliminary set of recommendations for its use in scholarly publishing. We hope that the recommendations and rationales set out here will help the scholarly community navigate toward a deeper understanding of the strengths, limits, and challenges of AI for responsible scholarly work.
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20
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Kaebnick GE, Magnus DC, Kao A, Hosseini M, Resnik D, Dubljević V, Rentmeester C, Gordijn B, Cherry MJ. Editors' Statement on the Responsible Use of Generative AI Technologies in Scholarly Journal Publishing. Hastings Cent Rep 2023; 53:3-6. [PMID: 37777997 DOI: 10.1002/hast.1507] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Generative artificial intelligence (AI) has the potential to transform many aspects of scholarly publishing. Authors, peer reviewers, and editors might use AI in a variety of ways, and those uses might augment their existing work or might instead be intended to replace it. We are editors of bioethics and humanities journals who have been contemplating the implications of this ongoing transformation. We believe that generative AI may pose a threat to the goals that animate our work but could also be valuable for achieving those goals. In the interests of fostering a wider conversation about how generative AI may be used, we have developed a preliminary set of recommendations for its use in scholarly publishing. We hope that the recommendations and rationales set out here will help the scholarly community navigate toward a deeper understanding of the strengths, limits, and challenges of AI for responsible scholarly work.
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Affiliation(s)
| | | | - Audiey Kao
- Editor in chief of the AMA Journal of Ethics
| | | | | | - Veljko Dubljević
- Editor in chief of the American Journal of Bioethics-Neuroscience
| | | | - Bert Gordijn
- Co-editor in chief of Medicine, Health Care and Philosophy
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21
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Kaebnick GE, Magnus DC, Kao A, Hosseini M, Resnik D, Dubljević V, Rentmeester C, Gordijn B, Cherry MJ, Maschke KJ, McMillan J, Rasmussen LM, Haupt L, Schüklenk U, Chadwick R, Diniz D. Editors' Statement on the Responsible Use of Generative AI Technologies in Scholarly Journal Publishing. Ethics Hum Res 2023; 45:39-43. [PMID: 37777979 DOI: 10.1002/eahr.500182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Generative artificial intelligence (AI) has the potential to transform many aspects of scholarly publishing. Authors, peer reviewers, and editors might use AI in a variety of ways, and those uses might augment their existing work or might instead be intended to replace it. We are editors of bioethics and humanities journals who have been contemplating the implications of this ongoing transformation. We believe that generative AI may pose a threat to the goals that animate our work but could also be valuable for achieving those goals. In the interests of fostering a wider conversation about how generative AI may be used, we have developed a preliminary set of recommendations for its use in scholarly publishing. We hope that the recommendations and rationales set out here will help the scholarly community navigate toward a deeper understanding of the strengths, limits, and challenges of AI for responsible scholarly work.
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Affiliation(s)
| | | | - Audiey Kao
- Editor in chief of the AMA Journal of Ethics
| | | | | | - Veljko Dubljević
- Editor in chief of the American Journal of Bioethics-Neuroscience
| | | | - Bert Gordijn
- Co-editor in chief of Medicine, Health Care and Philosophy
| | | | | | | | | | - Laura Haupt
- Managing editor of the Hastings Center Report and Ethics & Human Research
| | - Udo Schüklenk
- Joint editor in chief of Bioethics and of Developing World Bioethics
| | - Ruth Chadwick
- Joint editor in chief of Bioethics and the commissioning editor for ethics of the British Medical Bulletin
| | - Debora Diniz
- Joint editor in chief of Developing World Bioethics
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22
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Leung TI, de Azevedo Cardoso T, Mavragani A, Eysenbach G. Best Practices for Using AI Tools as an Author, Peer Reviewer, or Editor. J Med Internet Res 2023; 25:e51584. [PMID: 37651164 PMCID: PMC10502596 DOI: 10.2196/51584] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 08/28/2023] [Indexed: 09/01/2023] Open
Abstract
The ethics of generative artificial intelligence (AI) use in scientific manuscript content creation has become a serious matter of concern in the scientific publishing community. Generative AI has computationally become capable of elaborating research questions; refining programming code; generating text in scientific language; and generating images, graphics, or figures. However, this technology should be used with caution. In this editorial, we outline the current state of editorial policies on generative AI or chatbot use in authorship, peer review, and editorial processing of scientific and scholarly manuscripts. Additionally, we provide JMIR Publications' editorial policies on these issues. We further detail JMIR Publications' approach to the applications of AI in the editorial process for manuscripts in review in a JMIR Publications journal.
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Affiliation(s)
- Tiffany I Leung
- JMIR Publications, Inc, Toronto, ON, Canada
- Department of Internal Medicine (adjunct), Southern Illinois University School of Medicine, Springfield, IL, United States
| | | | | | - Gunther Eysenbach
- JMIR Publications, Inc, Toronto, ON, Canada
- University of Victoria, Victoria, BC, Canada
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Alqahtani T, Badreldin HA, Alrashed M, Alshaya AI, Alghamdi SS, Bin Saleh K, Alowais SA, Alshaya OA, Rahman I, Al Yami MS, Albekairy AM. The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Res Social Adm Pharm 2023:S1551-7411(23)00280-2. [PMID: 37321925 DOI: 10.1016/j.sapharm.2023.05.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
Artificial Intelligence (AI) has revolutionized various domains, including education and research. Natural language processing (NLP) techniques and large language models (LLMs) such as GPT-4 and BARD have significantly advanced our comprehension and application of AI in these fields. This paper provides an in-depth introduction to AI, NLP, and LLMs, discussing their potential impact on education and research. By exploring the advantages, challenges, and innovative applications of these technologies, this review gives educators, researchers, students, and readers a comprehensive view of how AI could shape educational and research practices in the future, ultimately leading to improved outcomes. Key applications discussed in the field of research include text generation, data analysis and interpretation, literature review, formatting and editing, and peer review. AI applications in academics and education include educational support and constructive feedback, assessment, grading, tailored curricula, personalized career guidance, and mental health support. Addressing the challenges associated with these technologies, such as ethical concerns and algorithmic biases, is essential for maximizing their potential to improve education and research outcomes. Ultimately, the paper aims to contribute to the ongoing discussion about the role of AI in education and research and highlight its potential to lead to better outcomes for students, educators, and researchers.
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Affiliation(s)
- Tariq Alqahtani
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Saudi Arabia; King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
| | - Hisham A Badreldin
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Mohammed Alrashed
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Abdulrahman I Alshaya
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Sahar S Alghamdi
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Saudi Arabia; King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Khalid Bin Saleh
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Shuroug A Alowais
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Omar A Alshaya
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Ishrat Rahman
- Department of Basic Dental Sciences, College of Dentistry, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Majed S Al Yami
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Abdulkareem M Albekairy
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
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24
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Buchanan A. Artificial intelligence-The next frontier of scientific publications? Aust Occup Ther J 2023. [PMID: 37127539 DOI: 10.1111/1440-1630.12877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 04/17/2023] [Indexed: 05/03/2023]
Affiliation(s)
- Angus Buchanan
- Associate Editor, Australian Occupational Therapy Journal, Melbourne, Australia
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25
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Gao CA, Howard FM, Markov NS, Dyer EC, Ramesh S, Luo Y, Pearson AT. Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers. NPJ Digit Med 2023; 6:75. [PMID: 37100871 PMCID: PMC10133283 DOI: 10.1038/s41746-023-00819-6] [Citation(s) in RCA: 92] [Impact Index Per Article: 92.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 03/30/2023] [Indexed: 04/28/2023] Open
Abstract
Large language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing. We gathered fifth research abstracts from five high-impact factor medical journals and asked ChatGPT to generate research abstracts based on their titles and journals. Most generated abstracts were detected using an AI output detector, 'GPT-2 Output Detector', with % 'fake' scores (higher meaning more likely to be generated) of median [interquartile range] of 99.98% 'fake' [12.73%, 99.98%] compared with median 0.02% [IQR 0.02%, 0.09%] for the original abstracts. The AUROC of the AI output detector was 0.94. Generated abstracts scored lower than original abstracts when run through a plagiarism detector website and iThenticate (higher scores meaning more matching text found). When given a mixture of original and general abstracts, blinded human reviewers correctly identified 68% of generated abstracts as being generated by ChatGPT, but incorrectly identified 14% of original abstracts as being generated. Reviewers indicated that it was surprisingly difficult to differentiate between the two, though abstracts they suspected were generated were vaguer and more formulaic. ChatGPT writes believable scientific abstracts, though with completely generated data. Depending on publisher-specific guidelines, AI output detectors may serve as an editorial tool to help maintain scientific standards. The boundaries of ethical and acceptable use of large language models to help scientific writing are still being discussed, and different journals and conferences are adopting varying policies.
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Affiliation(s)
- Catherine A Gao
- Division of Pulmonary and Critical Care, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
| | - Frederick M Howard
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Nikolay S Markov
- Division of Pulmonary and Critical Care, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Emma C Dyer
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Siddhi Ramesh
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Yuan Luo
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alexander T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA
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26
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Yeo-Teh NSL, Tang BL. An active aigiarism declaration for manuscript submission. Account Res 2023:1-2. [PMID: 36856345 DOI: 10.1080/08989621.2023.2185776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 02/25/2023] [Indexed: 03/02/2023]
Affiliation(s)
| | - Bor Luen Tang
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University Health System, Singapore
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27
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Affiliation(s)
- Michael Haman
- Department of Humanities, Czech University of Life Sciences Prague; Prague, Czech Republic
| | - Milan Školník
- Department of Humanities, Czech University of Life Sciences Prague; Prague, Czech Republic
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Flanagin A, Bibbins-Domingo K, Berkwits M, Christiansen SL. Nonhuman "Authors" and Implications for the Integrity of Scientific Publication and Medical Knowledge. JAMA 2023; 329:637-639. [PMID: 36719674 DOI: 10.1001/jama.2023.1344] [Citation(s) in RCA: 123] [Impact Index Per Article: 123.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Annette Flanagin
- Ms Flanagin is Executive Managing Editor, Dr Bibbins-Domingo is Editor in Chief, and Dr Berkwits is Electronic Editor, JAMA and the JAMA Network, and Ms Christiansen is Managing Editor, JAMA
| | - Kirsten Bibbins-Domingo
- Ms Flanagin is Executive Managing Editor, Dr Bibbins-Domingo is Editor in Chief, and Dr Berkwits is Electronic Editor, JAMA and the JAMA Network, and Ms Christiansen is Managing Editor, JAMA
| | - Michael Berkwits
- Ms Flanagin is Executive Managing Editor, Dr Bibbins-Domingo is Editor in Chief, and Dr Berkwits is Electronic Editor, JAMA and the JAMA Network, and Ms Christiansen is Managing Editor, JAMA
| | - Stacy L Christiansen
- Ms Flanagin is Executive Managing Editor, Dr Bibbins-Domingo is Editor in Chief, and Dr Berkwits is Electronic Editor, JAMA and the JAMA Network, and Ms Christiansen is Managing Editor, JAMA
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29
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Hu G. Challenges for enforcing editorial policies on AI-generated papers. Account Res 2023:1-3. [PMID: 36840450 DOI: 10.1080/08989621.2023.2184262] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 02/26/2023]
Abstract
ChatGPT, a chatbot released by OpenAI in November 2022, has rocked academia with its capacity to generate papers "good enough" for academic journals. Major journals such as Nature and professional societies such as the World Association of Medical Editors have moved fast to issue policies to ban or curb AI-written papers. Amid the flurry of policy initiatives, one important challenge seems to be overlooked: AI-generated papers are not easily discernible to the human eye, and we lack the right tools to implement the policies. Without such tools, the well-intentioned policies are likely to remain on paper.
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Affiliation(s)
- Guangwei Hu
- Department of English and Communication, The Hong Kong Polytechnic University, Hong Kong, China
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30
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Yeo-Teh NSL, Tang BL. Letter to editor: NLP systems such as ChatGPT cannot be listed as an author because these cannot fulfill widely adopted authorship criteria. Account Res 2023:1-3. [PMID: 36748354 DOI: 10.1080/08989621.2023.2177160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 02/08/2023]
Abstract
This letter to the editor suggests adding a technical point to the new editorial policy expounded by Hosseini et al. on the mandatory disclosure of any use of natural language processing (NLP) systems, or generative AI, in writing scholarly publications. Such AI systems should naturally also be forbidden from being named as authors, because they would not have fulfilled prevailing authorship guidelines (such as the widely adopted ICMJE authorship criteria).
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Affiliation(s)
- Nicole Shu Ling Yeo-Teh
- Research Compliance and Integrity Office, National University of Singapore, Singapore, Singapore
| | - Bor Luen Tang
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University Health System, Singapore, Singapore
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