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Attitudes and perceptions of medical researchers towards the use of artificial intelligence chatbots in the scientific process: an international cross-sectional survey
Open accessPublished version of a preprint
Abstract
Abstract Background Chatbots are artificial intelligence (AI) programs designed to simulate conversations with human users through text or speech. The use of artificial intelligence chatbots (AICs) in scientific research presents benefits and challenges. Although the stances of journals and publishing organizations on AIC use is increasingly clear, little is known about researchers’ perceptions of AICs in research. This survey study explores attitudes, familiarity, perceived benefits, limitations, and factors influencing adoption of AIC by researchers. Methods A cross-sectional online survey of published researchers was conducted. Corresponding authors and their e-mail addresses were identified by querying PubMed for articles (any type) published in a MEDLINE indexed journal in the most recent two months and using R script on PubMed metadata. e-Mail invitations were sent to 61560 study authors. The survey, administered on SurveyMonkey, opened on July 9, 2023, and closed on August 11, 2023. Respondents had 3 weeks to complete the survey and were sent 2 reminder e-mails during the weeks of July 17, 2023, and July 24, 2023. Results 2165 respondents completed the survey (4.0% response rate; 94% completion rate of those who responded). Most were familiar with the concept of AICs (n=1294/2138, 60.5%). About half had used an AIC previously for purposes relating to the scientific process (n=1107/2125, 52.1%). Only 244/2137 (11.4%) respondents reported that their institution offered training on using AI tools of whom 64/244 (26.2%) completed the training. 211/2131 (9.9%) reported that their institution implemented policies regarding AIC use in the scientific process. Most respondents expressed interest in learning more and receiving training on AIC use in the scientific process (n=1428/2048, 69.7%). Respondents had mixed opinions about the potential benefits of using AICs, whereas most agreed on their cons/challenges. Respondents agreed AICs were most beneficial in reducing the workload and administrative burden on researchers (n=1299/1941, 66.9%) and they were most concerned about the lack of understanding behind how AICs make decisions and generate responses (n=1484/1923, 77.2%). Conclusions Most respondents are familiar with AICs and half used AICs in their own research. Although there is clear interest in understanding how AICs can be used, many hesitate due to existing limitations. Little formal instruction on using AICs is available across academic institutions.
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“Surveys across the scientific community consistently show three dominant themes [4][5][6][7][8][9][10][11][12] .”
From Reimagining biomedical science workflows in the age of large language models (2026) · cites this paper as [5]
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“Second, the same surveys reveal widespread uncertainty about how to begin.Many researchers report limited familiarity with existing tools, limited confidence in their own ability to use them responsibly, and limited institutional guidance on best practices.”
From Reimagining biomedical science workflows in the age of large language models (2026) · cites this paper as [5]
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“Different surveys report mixed, but generally increasing adoption of AI by the scientific community.”
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“First, interest in using AI to support research is extremely high (Fig. 1).”
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References 27 · Cited by 64
Research works printed but not linked: 9
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- Yang, 2020, Estimating the deep replicability of scientific findings using human and artificial intelligence, Proc Natl Acad Sci USA, 117, 10762, 10.1073/pnas.1909046117 · DOI
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Metadata
- Authors
- Ng, J. Y., Maduranayagam, S. G., Suthakar, N., Li, A., Lokker, C., Iorio, A., Haynes, R. B., & Moher, D.
- Affiliations
- Ottawa Hospital Research Institute (CA)
- McMaster University (CA)
- University of Ottawa (CA)
- Corresponding author
- Ng, Jeremy Y
- ORCID
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2025-01
- Volume
- 7
- Issue
- 1
- Pages
- e94-e102
- ISSN
- 2589-7500 (ISSN-L)
- OpenAlex
- W4404404328
- Cited by
- 64
- References
- 27
- Citations by year
- 2026: 30 · 2025: 32
- FWCI
- 5.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by-nc)
- Funders
- Ottawa Hospital Research Institute
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- AI chatbots, artificial intelligence in research, researcher perceptions, institutional AI policies, administrative workload, scientific method
- MeSH terms
- Adult, Artificial Intelligence, Attitude, Cross-Sectional Studies, Female, Humans, Male, Middle Aged, Perception, Surveys and Questionnaires, Research Personnel, Biomedical Research
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